Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Kato Mivule: An Overview of Adaptive Boosting – AdaBoostKato Mivule
AdaBoost is a machine learning algorithm that uses multiple weak learners to create a strong learner. It works by assigning higher weights to misclassified examples from previous iterations and runs multiple iterations, each time adding a new weak learner that focuses on the examples with higher weights. The document presents an experiment using AdaBoost with decision stumps on a cancer dataset, finding a classification accuracy of 93.12% compared to 92.97% for decision stumps alone. ROC/AUC analysis showed AdaBoost with an AUC of 0.975 outperforming decision stumps with an AUC of 0.911, demonstrating that AdaBoost can create a more effective classifier than a single weak learner.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
This document provides an overview of machine learning classification and decision trees. It discusses key concepts like supervised vs. unsupervised learning, and how decision trees work by recursively partitioning data into nodes. Random forest and gradient boosted trees are introduced as ensemble methods that combine multiple decision trees. Random forest grows trees independently in parallel while gradient boosted trees grow sequentially by minimizing error from previous trees. While both benefit from ensembling, gradient boosted trees are more prone to overfitting and random forests are better at generalizing to new data.
This document discusses various techniques for data mining classification including rule-based classifiers, nearest neighbor classifiers, Bayes classifiers, artificial neural networks, and ensemble methods. Rule-based classifiers use if-then rules to classify records while nearest neighbor classifiers classify new records based on their similarity to training records. Bayes classifiers use Bayes' theorem to calculate conditional probabilities while artificial neural networks are assemblies of interconnected nodes that learn weights to classify data. Ensemble methods construct multiple classifiers and aggregate their predictions to improve accuracy.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
Kato Mivule: An Overview of Adaptive Boosting – AdaBoostKato Mivule
AdaBoost is a machine learning algorithm that uses multiple weak learners to create a strong learner. It works by assigning higher weights to misclassified examples from previous iterations and runs multiple iterations, each time adding a new weak learner that focuses on the examples with higher weights. The document presents an experiment using AdaBoost with decision stumps on a cancer dataset, finding a classification accuracy of 93.12% compared to 92.97% for decision stumps alone. ROC/AUC analysis showed AdaBoost with an AUC of 0.975 outperforming decision stumps with an AUC of 0.911, demonstrating that AdaBoost can create a more effective classifier than a single weak learner.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
This document discusses machine learning and artificial intelligence. It defines machine learning as a branch of AI that allows systems to learn from data and experience. Machine learning is important because some tasks are difficult to define with rules but can be learned from examples, and relationships in large datasets can be uncovered. The document then discusses areas where machine learning is influential like statistics, brain modeling, and more. It provides an example of designing a machine learning system to play checkers. Finally, it discusses machine learning algorithm types and provides details on the AdaBoost algorithm.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
Machine learning basics using trees algorithm (Random forest, Gradient Boosting)Parth Khare
This document provides an overview of machine learning classification and decision trees. It discusses key concepts like supervised vs. unsupervised learning, and how decision trees work by recursively partitioning data into nodes. Random forest and gradient boosted trees are introduced as ensemble methods that combine multiple decision trees. Random forest grows trees independently in parallel while gradient boosted trees grow sequentially by minimizing error from previous trees. While both benefit from ensembling, gradient boosted trees are more prone to overfitting and random forests are better at generalizing to new data.
This document discusses various techniques for data mining classification including rule-based classifiers, nearest neighbor classifiers, Bayes classifiers, artificial neural networks, and ensemble methods. Rule-based classifiers use if-then rules to classify records while nearest neighbor classifiers classify new records based on their similarity to training records. Bayes classifiers use Bayes' theorem to calculate conditional probabilities while artificial neural networks are assemblies of interconnected nodes that learn weights to classify data. Ensemble methods construct multiple classifiers and aggregate their predictions to improve accuracy.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
The document examines using a nearest neighbor algorithm to rate men's suits based on color combinations. It trained the algorithm on 135 outfits rated as good, mediocre, or bad. It then tested the algorithm on 30 outfits rated by a human. When trained on 135 outfits, the algorithm incorrectly rated 36.7% of test outfits. When trained on only 68 outfits, it incorrectly rated 50% of test outfits, showing larger training data improves accuracy. It also tested using HSL color representation instead of RGB with similar results.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Decision tree, softmax regression and ensemble methods in machine learningAbhishek Vijayvargia
This document discusses decision trees, softmax regression, and ensemble methods in machine learning. It provides details on how decision trees use information gain to split nodes based on attributes. Softmax regression is described as a generalization of logistic regression for multi-class classification problems. Ensemble methods like bagging, random forests, and boosting are covered as techniques that improve performance by combining multiple models.
Introduction to Some Tree based Learning MethodHonglin Yu
Random Forest, Boosted Trees and other ensemble learning methods build multiple models to improve predictive performance over single models. They combine "weak learners" like decision trees into a "strong learner". Random Forest adds randomness by selecting a random subset of features at each split. Boosting trains trees sequentially on weighted data from previous trees. Both reduce variance compared to bagging. Random Forest often outperforms Boosting while being faster to train. Neural networks can also be viewed as an ensemble method by combining simple units.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
Unsupervised Learning Techniques to Diversifying and Pruning Random ForestMohamed Medhat Gaber
The document discusses techniques for diversifying and pruning random forests for data classification. It covers background topics on data classification, ensemble classification, and random forests. It then describes two proposed techniques: CLUB-DRF which uses clustering to build a diverse random forest, and LOFB-DRF which uses outlier scoring. The document concludes with a summary and discussion of future work.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
This document demonstrates clustering and regression techniques using the Weka data mining software. It shows how Weka can be used to cluster 600 bank customer records into 6 groups based on attributes like age, income, family status, etc. It also uses Weka to create a linear regression model to predict house prices based on attributes like size, number of bedrooms, lot size, and more. Overall, the document shows how Weka allows easy implementation of common data mining algorithms and visualization of results.
IRJET- Evaluation of Classification Algorithms with Solutions to Class Imbala...IRJET Journal
This document discusses evaluating various classification algorithms to address class imbalance problems using the bank marketing dataset in WEKA. It first introduces data mining and classification algorithms like decision trees, naive Bayes, neural networks, support vector machines, logistic regression and random forests. It then discusses the class imbalance problem that occurs when one class is underrepresented. To address this, it explores sampling techniques like random under-sampling of the majority class, random over-sampling of the minority class, and SMOTE. It uses these techniques on the bank marketing dataset to evaluate the algorithms based on metrics like precision, recall, F1-score, ROC and AUCPR for the minority class.
Active learning aims to improve machine learning models using less training data by strategically selecting the most informative data points to be labeled. It is important because manually labeling data can be time-consuming and expensive. The core problem is how to actively select the most informative training points to query labels for. Different active learning methods, such as using neural networks, Bayesian models, and support vector machines, aim to query points that the current model is most uncertain about. Combining active learning with expectation maximization using a large pool of unlabeled data can improve text classification when only a small amount of labeled training data is available.
Minimizing Musculoskeletal Disorders in Lathe Machine WorkersWaqas Tariq
In production units, workers work under tough conditions to perform the desired task. These tough conditions normally give rise to various musculoskeletal disorders within the workers. These disorders emerge within the workers body due to repetitive lifting, differential lifting height, ambient conditions etc. For the minimization of musculoskeletal disorders it is quite difficult to model them with mathematical difference or differential equations. In this paper the minimization of musculoskeletal disorders problem has been formulated using fuzzy technique. It is very difficult to train non linear complex musculoskeletal disorders problem, hence in this paper a non linear fuzzy model has been developed to give solutions to these non linearities. This model would have the capability of representing solutions for minimizing musculoskeletal disorders needed for workers working in the production units.
This document discusses using fuzzy set theory and decision trees to predict student performance. It proposes using fuzzy sets to represent numeric student data like test scores and attendance to allow for imprecise values. A decision tree is generated on this fuzzy data set to classify students as passing or failing. The fuzzy decision tree achieves an accuracy of 81.5% compared to 76% for a non-fuzzy decision tree, indicating fuzzy sets improve predictive performance. Location, attendance, and prior academic performance were identified as important factors impacting student results.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
1) The document describes using a master-slave voting technique for supervised word sense disambiguation (WSD). It combines three approaches: Decision List as the master approach and Naive Bayes and AdaBoost as slave approaches.
2) Two experiments are conducted. The first combines Naive Bayes and Decision List, improving Naive Bayes' accuracy. The second combines AdaBoost and Decision List, further increasing accuracy.
3) A third experiment combines all three approaches, with Decision List as the master. As expected, this achieves the highest accuracy compared to the individual approaches.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
The document examines using a nearest neighbor algorithm to rate men's suits based on color combinations. It trained the algorithm on 135 outfits rated as good, mediocre, or bad. It then tested the algorithm on 30 outfits rated by a human. When trained on 135 outfits, the algorithm incorrectly rated 36.7% of test outfits. When trained on only 68 outfits, it incorrectly rated 50% of test outfits, showing larger training data improves accuracy. It also tested using HSL color representation instead of RGB with similar results.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Decision tree, softmax regression and ensemble methods in machine learningAbhishek Vijayvargia
This document discusses decision trees, softmax regression, and ensemble methods in machine learning. It provides details on how decision trees use information gain to split nodes based on attributes. Softmax regression is described as a generalization of logistic regression for multi-class classification problems. Ensemble methods like bagging, random forests, and boosting are covered as techniques that improve performance by combining multiple models.
Introduction to Some Tree based Learning MethodHonglin Yu
Random Forest, Boosted Trees and other ensemble learning methods build multiple models to improve predictive performance over single models. They combine "weak learners" like decision trees into a "strong learner". Random Forest adds randomness by selecting a random subset of features at each split. Boosting trains trees sequentially on weighted data from previous trees. Both reduce variance compared to bagging. Random Forest often outperforms Boosting while being faster to train. Neural networks can also be viewed as an ensemble method by combining simple units.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
Unsupervised Learning Techniques to Diversifying and Pruning Random ForestMohamed Medhat Gaber
The document discusses techniques for diversifying and pruning random forests for data classification. It covers background topics on data classification, ensemble classification, and random forests. It then describes two proposed techniques: CLUB-DRF which uses clustering to build a diverse random forest, and LOFB-DRF which uses outlier scoring. The document concludes with a summary and discussion of future work.
An introductory course on building ML applications with primary focus on supervised learning. Covers the typical ML application cycle - Problem formulation, data definitions, offline modeling, platform design. Also, includes key tenets for building applications.
Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
Machine learning involves developing systems that can learn from data and experience. The document discusses several machine learning techniques including decision tree learning, rule induction, case-based reasoning, supervised and unsupervised learning. It also covers representations, learners, critics and applications of machine learning such as improving search engines and developing intelligent tutoring systems.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
This document demonstrates clustering and regression techniques using the Weka data mining software. It shows how Weka can be used to cluster 600 bank customer records into 6 groups based on attributes like age, income, family status, etc. It also uses Weka to create a linear regression model to predict house prices based on attributes like size, number of bedrooms, lot size, and more. Overall, the document shows how Weka allows easy implementation of common data mining algorithms and visualization of results.
IRJET- Evaluation of Classification Algorithms with Solutions to Class Imbala...IRJET Journal
This document discusses evaluating various classification algorithms to address class imbalance problems using the bank marketing dataset in WEKA. It first introduces data mining and classification algorithms like decision trees, naive Bayes, neural networks, support vector machines, logistic regression and random forests. It then discusses the class imbalance problem that occurs when one class is underrepresented. To address this, it explores sampling techniques like random under-sampling of the majority class, random over-sampling of the minority class, and SMOTE. It uses these techniques on the bank marketing dataset to evaluate the algorithms based on metrics like precision, recall, F1-score, ROC and AUCPR for the minority class.
Active learning aims to improve machine learning models using less training data by strategically selecting the most informative data points to be labeled. It is important because manually labeling data can be time-consuming and expensive. The core problem is how to actively select the most informative training points to query labels for. Different active learning methods, such as using neural networks, Bayesian models, and support vector machines, aim to query points that the current model is most uncertain about. Combining active learning with expectation maximization using a large pool of unlabeled data can improve text classification when only a small amount of labeled training data is available.
Minimizing Musculoskeletal Disorders in Lathe Machine WorkersWaqas Tariq
In production units, workers work under tough conditions to perform the desired task. These tough conditions normally give rise to various musculoskeletal disorders within the workers. These disorders emerge within the workers body due to repetitive lifting, differential lifting height, ambient conditions etc. For the minimization of musculoskeletal disorders it is quite difficult to model them with mathematical difference or differential equations. In this paper the minimization of musculoskeletal disorders problem has been formulated using fuzzy technique. It is very difficult to train non linear complex musculoskeletal disorders problem, hence in this paper a non linear fuzzy model has been developed to give solutions to these non linearities. This model would have the capability of representing solutions for minimizing musculoskeletal disorders needed for workers working in the production units.
This document discusses using fuzzy set theory and decision trees to predict student performance. It proposes using fuzzy sets to represent numeric student data like test scores and attendance to allow for imprecise values. A decision tree is generated on this fuzzy data set to classify students as passing or failing. The fuzzy decision tree achieves an accuracy of 81.5% compared to 76% for a non-fuzzy decision tree, indicating fuzzy sets improve predictive performance. Location, attendance, and prior academic performance were identified as important factors impacting student results.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
1) The document describes using a master-slave voting technique for supervised word sense disambiguation (WSD). It combines three approaches: Decision List as the master approach and Naive Bayes and AdaBoost as slave approaches.
2) Two experiments are conducted. The first combines Naive Bayes and Decision List, improving Naive Bayes' accuracy. The second combines AdaBoost and Decision List, further increasing accuracy.
3) A third experiment combines all three approaches, with Decision List as the master. As expected, this achieves the highest accuracy compared to the individual approaches.
Supervised WSD Using Master- Slave Voting Techniqueiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Machine learning algorithms show promise in improving medical image analysis and diagnosis by helping physicians more accurately interpret images. Such algorithms can be trained using labeled medical image data to learn the differences between benign and malignant tumors, and then apply that learning to analyze new images and predict the likelihood of tumors being benign or malignant. However, it is important to address the potential pitfalls of machine learning and ensure its safe and effective use in medical applications.
Gradient boosted trees are an ensemble machine learning technique that produces a prediction model as an ensemble of weak prediction models, typically decision trees. It builds models sequentially to minimize a loss function using gradient descent. Each new model is fit to the negative gradient of the loss function to reduce error. This allows weak learners to be combined into a stronger learner with better predictive performance than a single decision tree. Key advantages are it is fast, easy to tune, and achieves good performance.
The document describes a machine learning toolbox developed using Python that implements and compares several supervised machine learning algorithms, including Naive Bayes, K-nearest neighbors, decision trees, SVM, and neural networks. The toolbox allows users to test algorithms on various datasets, including Iris and diabetes data, and compare the accuracy results. Testing on these datasets showed Naive Bayes and K-nearest neighbors had the highest average accuracy rates, while neural networks and decision trees showed more variable performance depending on parameters and dataset splits. The toolbox is intended to help users evaluate which algorithms best fit their datasets.
The learning method used by our approach is usually known in the artificial intelligence community as learning by observation, imitation learning, learning from demonstration, programming by demonstration, learning by watching or learning by showing. For consistency, learning by observation will be used from here on.
Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can represent learning problems with known or unknown structures and fully or partially observable variables. Belief networks use localized representations, whereas neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, such as Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received. Genetic algorithms evolve individuals to successful solutions measured by fitness functions. Explanation-based learning speeds up programs by reusing results of prior computations.
Bayesian learning views hypotheses as intermediaries between data and predictions. Belief networks can have known or unknown structures and hidden variables. Belief networks represent knowledge locally while neural networks use distributed representations. Reinforcement learning uses rewards to learn successful agent functions, like Q-learning which learns action-value functions. Active learning agents consider actions, outcomes, and how actions affect rewards received to build an environment model and utility function. Explanation based learning speeds up programs by reusing results of prior computations to make knowledge bases more efficient.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
IRJET - A Survey on Machine Learning Algorithms, Techniques and ApplicationsIRJET Journal
This document discusses machine learning algorithms, techniques, and applications. It begins with an introduction to machine learning and different types of learning including supervised learning, unsupervised learning, reinforcement learning, and others. It then groups various machine learning algorithms based on similarities and compares the performance of popular algorithms like Naive Bayes, support vector machines, and decision trees. The document concludes that machine learning researchers aim to design more efficient algorithms that can perform better across different domains.
This document summarizes and compares different classification algorithms that can be used for disease prediction in data mining. It first introduces disease prediction and classification processes. It then reviews related works that have used various classification algorithms like random forest, support vector machine, and naive Bayes for tasks like disease diagnosis, text classification, and rainfall forecasting. The document also discusses supervised, unsupervised, and semi-supervised machine learning. It provides details on support vector machine and random forest algorithms, describing how each works and is used for classification. Finally, it analyzes the random forest algorithm construction process.
This document summarizes a machine learning project to predict insurance claim severities for the Kaggle "Allstate Claims Severity" competition. It describes the dataset, preprocessing steps including one-hot encoding and outlier removal. A deep neural network model was trained using H2O. Hyperparameters were optimized in 4 phases: activation function, network architecture, outliers/epochs, and learning rate. 21 model submissions achieved test MAEs from 1,169 to 1,292, outperforming random forest benchmarks. Rectifier activation, 3 hidden layers of 1000 neurons total, removing outliers, 100 epochs, and a learning rate of 0.0001 produced strong results.
This document describes a hybrid expert system called GAMBLE that uses a genetic algorithm to help students get admitted to the best engineering program based on their skills and the branch requirements. GAMBLE calculates each student's aptitude for different branches, suggests the most suitable option, and learns over time to improve its recommendations by modifying the branch thresholds and classifier strings based on past student admissions. The system aims to model human learning aspects and help more students get placed in the appropriate engineering programs.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
Opinion mining framework using proposed RB-bayes model for text classicationIJECEIAES
Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
NITW_Improving Deep Neural Networks (1).pptxDrKBManwade
This document provides an overview of techniques for training deep neural networks. It discusses neural network parameters and hyperparameters, regularization strategies like L1 and L2 norm penalties, dropout, batch normalization, and optimization methods like mini-batch gradient descent. The key aspects covered are distinguishing parameters from hyperparameters, techniques for reducing overfitting like regularization and early stopping, and batch normalization for reducing internal covariate shift during training.
NITW_Improving Deep Neural Networks.pptxssuserd23711
This document provides an overview of techniques for training deep neural networks. It discusses neural network parameters and hyperparameters, regularization strategies like L1 and L2 norm penalties, dropout, batch normalization, and optimization methods like mini-batch gradient descent. The key aspects covered are distinguishing parameters from hyperparameters, techniques for reducing overfitting like regularization and early stopping, and batch normalization for reducing internal covariate shift during training.
A New Active Learning Technique Using Furthest Nearest Neighbour Criterion fo...ijcsa
Active learning is a supervised learning method that is based on the idea that a machine learning algorithm can achieve greater accuracy with fewer labelled training images if it is allowed to choose the image from which it learns. Facial age classification is a technique to classify face images into one of the several predefined age groups. The proposed study applies an active learning approach to facial age classification which allows a classifier to select the data from which it learns. The classifier is initially trained using a small pool of labeled training images. This is achieved by using the bilateral two dimension linear discriminant analysis. Then the most informative unlabeled image is found out from the unlabeled pool using the furthest nearest neighbor criterion, labeled by the user and added to the
appropriate class in the training set. The incremental learning is performed using an incremental version of bilateral two dimension linear discriminant analysis. This active learning paradigm is proposed to be applied to the k nearest neighbor classifier and the support vector machine classifier and to compare the performance of these two classifiers.
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Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Maize Expert System
1. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
DOI : 10.5121/ijist.2012.2305 63
Implementation of Naive Bayesian Classifier and
Ada-Boost Algorithm Using Maize Expert System
Naveen Kumar Korada , N Sagar Pavan Kumar, Y V N H Deekshitulu
Email-Id: knaveenkumar35@gmail.com
Abstract
Machine learning [1] is concerned with the design and development of algorithms that allow computers to
evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy
less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase
accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner
several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used
to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm
iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input
into different classes with some attributes. Maize Expert System is developed to identify the diseases of
Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the
Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin
are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms
entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain
expert. Admin module may be used for maintenance of the system.
Keywords
Expert Systems, Machine Learning, Ada-Boost, Naïve Bayesian Classifier, Maize, JSP and MYSQL
I. Introduction
A. Machine Learning
Machine learning[2, 3, 4 and 6], a branch of artificial intelligence, is a scientific discipline
concerned with the design and development of algorithms that allow computers to evolve
behaviors based on empirical data, such as from sensor data or databases. The key issue in the
development of Expert Systems is the knowledge acquisition for building its knowledge base.
One simple technique for acquiring the knowledge is direct injection method in which the
knowledge is collected from the domain experts by conducting programmed interviews and
entering it in an appropriate place manually. But it is difficult process and time consuming.
Instead, machine learning algorithms are used by making the systems learn from their past
experiences. The goal of machine learning is to program computers to use training data or past
experience to solve a given problem. Effective algorithms have been invented for certain types of
learning tasks. Many practical computer programs have been developed to exhibit useful types of
learning and significant commercial applications have begun to appear. Machine learning refers
2. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
64
to the changes in systems that perform tasks associated with artificial intelligence (AI). Such
tasks involve recognition, diagnosis, planning, robot control, prediction, etc. Some of the
machines learning algorithms are Genetic Algorithm [16], ID3 [17], ABC algorithm [18],
Artificial Neural Networks [19] and C4.5 Algorithm [20] etc.
B. Expert Systems
An expert system [5] is a computer system that emulates the decision-making ability of a human
expert, i.e. it acts in all respects as a human expert. Expert systems have emerged from early work
in problem solving, mainly because of the importance of domain-specific knowledge. The expert
knowledge must be obtained from specialists or other sources of expertise, such as texts, journal
articles, and data bases. Expert system receives facts from the user and provides expertise in
return. The user interacts with the system through a user interface, constructed by using menus,
natural language or any other style of interaction. The rules collected from the domain experts are
encoded in the form of Knowledge base. The inference engine may infer conclusions from the
knowledge base and the facts supplied by the user. Expert systems may or may not have learning
components. A series of Expert advisory systems [12], [13], [15] were developed in the field of
agriculture and implemented in Indiakisan.net [14].
C. Adaptive Boosting (Ada-Boost) Algorithm
Ada-Boost [8, 11], short for Adaptive Boosting, is a machine learning algorithm, formulated by
Yoav Freund and Robert Schapire [7]. It is a meta-algorithm, and can be used in conjunction with
many other learning algorithms to improve their performance. Generally learning algorithms are
either strong classifiers or weak classifiers. Strong classification algorithms use the techniques
such as ANN, SVM etc. Weak classification algorithms use the techniques such as Decision trees,
Bayesian Networks, Random forests etc. Ada-Boost is adaptive because the instances
misclassified by previous classifier are reorganized into the subsequent classifier. Ada-Boost is
sensitive to noisy data and outliers. The boosting algorithm begins by assigning equal weight to
all instances in the training data. It then calls the learning algorithm to form a classifier for this
data, and reweighs each instance according to the classifier's output. The weight of correctly
classified instances is decreased, and that of misclassified ones is increased. This produces a set
of easy instances with low weight, and a set of hard ones with high weight. In the next iteration, a
classifier is built for the reweighed data, which consequently focuses on classifying the hard
instances correctly. Then the instances weights are increased or decreased according to the output
of this new classifier. Here there are two possibilities: The first one is harder instances may
become even harder and easier instances may become even easier. The second possibility is the
harder instances may become easier and easier instances may become harder. After all weights
have been updated, they are renormalized so that their sum remains the same as it was before.
After all iterations, the final hypothesis value is calculated.
The pseudo code for Ada-Boost algorithm is given as below
• Input: a set S, of ‘m’ labeled examples: S= ((xi,yi), i=(1,2,…,m)), with labels in Y.
• Learn (a learning algorithm)
• A constant L.
[1] Initialize for all i: wj(i)=1/m // initialize the weights
3. International Journal of Information Sciences and Techniques (IJIST) Vol.2, No.3, May 2012
65
[2] for j=1 to L do
[3] for all i: // compute normalized weights
pi =
wi
∑ wi
[4] hj:=Naïve-Bayesian(S,pj) // call weak Learn with normalized weights
[5] Calculate the error of hj
ε =
4. pi[h
x ≠ y]
[6] if ε
then
[7] L=j-1
[8] go to 12
[9]
β
=
ε
1 − ε
[10] for all i: // compute new weights
wi = wiβ
[ ! ]
[11] end for
12] Output:
h#$%x =
5. log
1
β
* [hx = y]
+
,
!∈.
$/0
$
D. Naïve Bayesian Classifier (Weak learner)
Naïve Bayes Classifier is a simple probabilistic A Naive Bayes Classifier [9] is a simple
probabilistic classifier based on applying Bayes' theorem with strong (naive) independence
assumptions. A more descriptive term for the underlying probability model would be
independent feature model. Depending on the precise nature of the probability model, Naive
Bayes classifiers can be trained very efficiently in a supervised learning setting. In spite of their
naive design and apparently over-simplified assumptions, Naïve Bayes classifiers have worked
quite well in many complex real-world situations. A comprehensive comparison with other
classification methods showed that Bayes classification is outperformed by more current
approaches, such as boosted trees or random forests [10].
An advantage of the Naive Bayes classifier is that it only requires a small amount of training data
to estimate the parameters necessary for classification.
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66
E. KNOWLEDGE BASE
Expert system knowledge base contains a formal representation of the information provided by
the domain experts. The information is collected from the domain experts by conducting
programmed interviews. The Knowledge Base of the Maize Expert System contains the diseases
and the symptoms for corresponding diseases and the cure for those diseases is encoded in the
form of rules. The symptoms and diseases occurred in maize crop are represented in tabular
format as below.
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67
I. PROPOSED ADA-BOOST ALGORITHM:
The Proposed Ada-Boost Algorithm uses the Naïve-Bayes classifier as weak learner and it uses
the training data and the weights are initialized based on the number of classifiers i.e. the weights
of the each class is equal to the fraction of the total number of classifiers. Select ‘T’, the number
of rounds the algorithm has to run iteratively by adjusting the weights. In each round the weak
learner is called based on the given input and the weights for each classifier and it generates a
new hypothesis ‘hj’ in each hypothesis and the weight and the error is calculated based on the
obtained hypothesis and based on the error value obtained the new weights are calculated by
using the formula given below
wi = wiβ
[ ! ]
Where βj is error coefficient.
The weak learner is called by using the new weights. The process is repeated until the error value
greater than ½ or the number of iterations completes. And finally, the hypothesis value is
calculated by using the given formula.
h#$%x =
8. 2log
1
β
3 [hx = y]
+
,
!∈.
$/0
$
The flow diagram of the proposed Ada-Boost algorithm used in development of this Expert
Advisory System is shown in Figure. 1. Flow chart of the Ada-Boost Algorithm
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A. Simple Example
The working of the proposed system is explained by considering the 10 symptoms as input. It is
explained as follows
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• Encode Solution: Just use 10 bits (1 or 0).
• Generate input.
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
1 0 1 0 0 0 1 0 1 0
• Initialize the weights wi based on the classifiers. Consider there are 5 classifiers, wi= 1/5.
• Select the value for ‘T’, the number of iterations.
• In each and every, iterations the hypothesis value ‘hj’ is to be calculated.
• The error value is calculated by adding the probabilities value of the remaining diseases
with their corresponding weights.
• Based on the error value the algorithm is repeated repeatedly for ‘T’ times by adjusting
the weights.
1. Hypothesis Values Using Naïve Bayesian Classifier
The probability densities for each disease is calculated using the Naïve Bayesian classifier as
follows
P (Disease1/s1 ...s10) = P (Disease1)*P (s1/Disease1)*P (s2/Disease2)….P
(s10/Disease)
By using the above equation for the given input string
P(Corn Streak Virus/1,0,1,0,0,0,1,0,1,0)= 0.15002
P(Sorghum Down Mildew/1,0,1,0,0,0,1,0,1,0)=0.0
P(Postfloweringstalk rot/1,0,1,0,0,0,1,0,1,0)=0.01
P(Phaesophearia Leaf spot/1,0,1,0,0,0,1,0,1,0)=0.02922
P(Alternaria Leaf Spot/1,0,1,0,0,0,1,0,1,0)=0.022
2. Hypothesis Values Using Ada-Boost Algorithm
The probability densities for each disease is calculated using the Ada-Boost algorithm is as
follows
P(Disease1/s1,..s10)= P(Disease1)*P(s1/Disease1)*P(s2/Disease2)….P(s10/Disease)
By using the above equation for the given input string
P(Corn Streak Virus/1,0,1,0,0,0,1,0,1,0)= 0.20000002
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70
P(Sorghum Down Mildew/1,0,1,0,0,0,1,0,1,0)=0.0
P(Postfloweringstalk rot/1,0,1,0,0,0,1,0,1,0)=0.029626261
P(Phaesophearia Leaf spot/1,0,1,0,0,0,1,0,1,0)=0.12922
P(Alternaria Leaf Spot/1,0,1,0,0,0,1,0,1,0)=0.122
After ‘n’ iterations, the final probability value for disease Corn Streak Virus is greater than all the
remaining diseases hence the final hypothesis classifies the given data to the class with name
“Corn Streak Virus”.
II. COMPARATIVE STUDY
The hypothesis values computed by the Naïve-Bayesian Classifier and the Ada-Boost algorithm
are tabulated in table 1. The hypothesis value is maximum to Corn Streak Virus.
H.AB= Hypothesis value of Ada-Boost algorithm
H.NB= Hypothesis value of Naïve-Bayesian Classifier
Increase in
Accuracy % = (H.AB – H.NB)/H.NB *100.
From the table
H.AB for Corn Streak Virus=0.20000002
H.NB for Corn Streak Virus=0.15002
Increase in accuracy for Corn Streak Virus= 33%
Table 1: Hypothesis Values of the Algorithms
Disease Name
Hypothesis Value
using Naïve-Bayesian
Classifier
Hypothesis Value using Ada-
Boost Algorithm
Corn Streak Virus 0.15002 0.2002
Sorghum Down
Mildew
0.0 0.0
Post flowering stalk rot 0.01 0.0292
Phaesophearia Leaf
spot
0.0291 0.12922
Alternaria Leaf Spot 0.022 0.122
Based on the hypothesis values, the error values are calculated. A graph is drawn taking the
number of iterations on X-axis and the error values of the both Naïve-Bayesian Classifier and the
Ada-Boost algorithms are taken on the Y- axis (figure 2).
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71
Figure 2: Graph describing the performance of Ada-Boost Algorithm
It can be observed that, as the number of iterations increases the miss-classification error values is
decreased in Ada-Boost algorithm than pure Naïve-Bayesian classifier algorithm.
III. MAIZE EXPERT SYSTEM ARCHITECTURE
The Proposed architecture of the Maize Expert System consists of Rule Based Expert System,
Ada-Boost algorithm and Knowledge Base which are used in the inference mechanism. It is
represented in the figure 3
Figure 3: Proposed architecture of Expert System
0
0.05
0.1
0.15
0.2
0.25
0.3
1 5 10 15 20 25
Error Values
with using
AdaBoost
Algorithm
Error
Values
No.of Iterations
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The User Interface of the Maize expert system consisting of three different interfaces namely,
End-user/farmer, Expert and Admin, is presented here. End-user/farmer module may be used for
identifying the diseases for the symptoms entered by the farmer. Expert module may be used for
adding rules and questions to data set by a domain expert. Admin module may be used for
maintenance of the system.
IV.RESULTS
Figure 4 Screen for selecting symptoms in Maize Expert System
Description: In this screen shot, the user can submit the observed symptoms to the maize advisory
system through online by selecting the appropriate radio buttons for the processing of the
symptoms observed.
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73
Figure 5 Displaying advices to the farmer
Description: In this screen shot, the algorithm takes the input given by the user and classifies the
input into the Corn Streak Virus class and generating the following advices.
Effected With: Corn Streak Virus
Cure is: Spraying of insecticides endosulfan 35EC @ 600-750 ml/n5g.
V.CONCLUSIONS
According to the results, the performance of the Naïve- Bayesian classifier (weak learner) is
improved by 33.33% with the help of Ada-Boost algorithm and it generates accurate results by
reducing the miss-classification error values by increasing the iterations. Using this algorithm as
inference mechanism a Maize Expert Advisory System is developed using Java Server Pages
(JSP) and MYSQL database as backend for classifying the given symptoms given by the farmers
into the corresponding disease class and suggest advices to the improvement of the crop.
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Authors
NAVEEN KUMAR KORADA received the Bachelor’s in Computer Science
Engineering from JNTU and Master’s Degree in Computer Science Technology with
Artificial Intelligence and Robotics (CST with AI R) from Andhra University,
Visakhapatnam, and Andhra Pradesh, India. He is currently Software Engineer in TATA
BSS. He has two published papers in International Journals. His area of interest includes
Networks and Robotics.
N SAGAR PAVAN KUMAR received the Bachelor’s in Computer Science
Engineering from JNTU and Master’s Degree in Software Engineering (SE) from
GITAM University, Visakhapatnam, and Andhra Pradesh, India. He is currently
Assistant Professor in the Department of CSE, Vignan College of Engineering
(Women), Visakhapatnam.
Y V N H DEEKSHITULU received the Bachelor’s in Computer Science Engineering
from JNTU and Master’s Degree in Computer Science Technology with Artificial
Intelligence and Robotics (CST with AI R) from Andhra University, Visakhapatnam,
and Andhra Pradesh, India. He is currently Software Engineer in TATA BSS.