This presentation provides an overview of boosting approaches for classification problems. It discusses combining classifiers through bagging and boosting to create stronger classifiers. The AdaBoost algorithm is explained in detail, including its training and classification phases. An example is provided to illustrate how AdaBoost works over multiple rounds, increasing the weights of misclassified examples to improve classification accuracy. In conclusion, AdaBoost is highlighted as an effective approach for classification problems where misclassification has severe consequences by producing highly accurate strong classifiers.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Classifications & Misclassifications of EEG Signals using Linear and AdaBoost...IJARIIT
Epilepsy is one of the frequent brain disorder due to transient and unexpected electrical interruptions of brain. Electroencephalography (EEG) is one of the most clinically and scientifically exploited signals recorded from humans and very complex signal. EEG signals are non-stationary as it changes over time. So, discrete wavelet transform (DWT) technique is used for feature extraction. Classifications and misclassifications of EEG signals of linearly separable support vector machines are shown using training and testing datasets. Then AdaBoost support vector machine is used to get strong classifier.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
A brief presentation given on the basics of Ensemble Methods. Given as a 'Lightning Talk' during the 7th Cohort of General Assembly's Data Science Immersive Course
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Classifications & Misclassifications of EEG Signals using Linear and AdaBoost...IJARIIT
Epilepsy is one of the frequent brain disorder due to transient and unexpected electrical interruptions of brain. Electroencephalography (EEG) is one of the most clinically and scientifically exploited signals recorded from humans and very complex signal. EEG signals are non-stationary as it changes over time. So, discrete wavelet transform (DWT) technique is used for feature extraction. Classifications and misclassifications of EEG signals of linearly separable support vector machines are shown using training and testing datasets. Then AdaBoost support vector machine is used to get strong classifier.
This presentation is about Multiple Classifier System (Ensemble of Classifiers). At first tell about the general idea of decision making, then address reasons and rationales of using Multiple Classifier System, after that concentrate on designing Multiple Classifier System: 1.Create an Ensemble 2.Combining Classifiers.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Understanding Blackbox Prediction via Influence FunctionsSEMINARGROOT
Pang Wei Koh and Percy Liang
"Understanding Black-Box prediction via influence functions" ICML 2017 Best paper
References:
https://youtu.be/0w9fLX_T6tY
https://arxiv.org/abs/1703.04730
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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.
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Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Biological screening of herbal drugs: Introduction and Need for
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Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
3. Supervised learning is the machine learning task .
infer a function from labeled training data.
The training data consist of a set of training examples.
In supervised learning, each example is a pair
consisting of a input object and a desired output
value called a supervisory signal.
Optimal scenario ?
Target: generalize the learning algorithm from the
training data to unseen situation in reasonable way.
Introduction
4. Classification is a type of supervised learning.
Classification relies on a priori reference structures that
divide the space of all possible data points into a set of
classes that are usually, but not necessarily, non-
overlapping.
A very familiar example is the email spam-catching
system.
Classification
5. The main issue in the classification is miss
classification.
which leads to the under-fitting and over-fitting
problems.
Like in the case of spam filtering due to miss
classification the spam may be classified as not spam
which is not considerable sometime.
So the major issue here to improve the accuracy of
the classification.
Contd……
6. Combining classifiers makes the use of some weak
classifiers and combining such classifier gives a strong
classifier.
Combining Classifiers
8. Bagging (Bootstrap aggregating) operates using
bootstrap sampling.
Given a training data set D containing m examples,
bootstrap sampling draws a sample of training
examples, Di, by selecting m examples uniformly at
random with replacement from D. The replacement
means that examples may be repeated in Di.
Bagging
10. Training Phase
Initialize the parameters
D={Ф}
h=the number of classification
For k=1 to h
Take a bootstrap sample Sk from training set S
Build the classifier Dk using Sk as training set
D=DUDi
Return D
Classification Phase
Run D1,D2,………..Dk on the input k
The class with maximum number of vote is choosen as the label
for X.
Bagging Algorithm
11. Boosting has been a very successful technique for solving the
two-class classification problem.
It was first introduced by Freund & Schapire (1997), with their
AdaBoost algorithm .
Rather than just combining the isolated classifiers boosting use
the mechanism of increasing the weights of misclassified data in
preceding classifiers.
A weak learner is defined to be a classifier which is only slightly
correlated with the true classification.
In contrast, a strong learner is a classifier that is arbitrarily well-
correlated with the true classification.
Boosting
13. 1. Initialize the data weighting coefficients {Wn } by setting Wi =
1/n, for n=1,2……..,N
2. For m=1 to m
a. Fit a classifier y 𝑚(x) to the training data by minimizing the
weighted error function.
b. Evaluate the quantities
The term I(ym(xn)≠tn) is indication function has values 0/1, 0 if xn
is properly classified 1 if not so.
AdaBoost Algotithm
14. And use these to evaluate
c. Update the data weighting coefficients
3. Make predictions using the final model, which is given by
Contd….
15. Let us take following points training set having 10 points represented
by plus or minus.
Assumption is the original status is assign equal weight to all points.
Let us take following points training set having 10 points represented
by plus or minus.
Assumption is the original status is assign equal weight to all points.
i.e. W1
(1) =W1
(2 ) =…………….=W1
(10)=1/10.
Figure1. Training set consisting 10 samples
Example AdaBoost
16. Round 1: Three “plus” points are not correctly classified. They
are given higher weights.
Figure 2. First hypothesis h1 misclassified 3 plus.
Contd…..
17. And error term and learning rate for first hypothesis as:
𝜖1 =
0.1+0.1+0.1
1
= 0.30
𝛼1 =
1
2
ln 1 − 0.30
0.30
= 0.42
Now we calculate the weights of each data points for second hypothesis as:
Wn
(m+1)=?
1st, 2nd, 6th, 7th, 8th, 9th and 10th data points are classified properly so their
weight remains same.
i.e. W1
(2)=W2
(2)=W6
(2)=W7
(2)=W8==W9
(2)=W10
(2)= 0.1
but 3rd,4th and 5th data points are misclassified so higher weights are
provided to them as
W3
(2)=W4
(2)=W5
(2)=0.1*e0.42=0.15
Contd..
18. Round 2: Three “minuse” points are not correctly classified. They
are given higher weights.
Figure5. Second Hypothesis h2 misclassified 3 minus.
Contd……
19. 𝜀2 =
𝑜. 1 + 0.1 + 0.1
1.15
= 0.26
𝛼2 =
1
2
ln 1 − 0.26
0.26
= 0.52
Now calculating values Wn
(3) as
Here second hypothesis has misclassified 6th, 7th and 8th so they are
provided with higher weights as :
W6
(3)=W7
(3)= W8
(3)=0.1*e(0.52)=0.16
Whereas the data points 1,2,3,4,5,9,10 are properly classified so their
weights remains same as:
W1
(3)=W2
(3)=W9
(3)=W10
(3)= 0.1
W3
(3)=W4
(3)=W5
(3)=0.15
Cont….
20. Round 3:
Figure 5. Third hypothesis h3 misclassified 2 plus and 1 minus.
Contd…
23. Adaboost algorithm provides a strong classification
mechanism combining various weak classifiers resulting into
strong classifier which then is able to increase accuracy and
efficiency.
Final learner will have minimum error and maximum learning
rate resulting to the high degree of accuracy.
Hence, Adaboost algorithm can be used in such where
misclassification leads to dire consequences very successfully
at some extent.
Conclusions
24. [1]. Eric Bauer“An Empirical Comparison of Voting Classification Algorithms: Bagging,
Boosting, and Variants “, Computer Science Department, Stanford University Stanford CA,
94305, 1998.
[2]. K. Tumer and J. Ghosh, “Classifier Combining: Analytical Results and Implications,” Proc
Nat’l Conf. Artificial Intelligence , Portland,Ore.,1996.
[3]. Paul Viola and Michael Jones,” Fast and Robust Classification using Asymmetric AdaBoost
and a Detector Cascade”, Mistubishi Electric Research Lab Cambridge, MA.
[4]. P´adraig Cunningham, Matthieu Cord, and Sarah Jane Delany,” Machine learning
techniques for multiledia case studies on organization and retrival” Cord,M,
Cunningham,2008.
[5]. Trevor Hastie,” Multi-class AdaBoost” Department of Statistics Stanford University , CA
94305”,January 12, 2006.
[6]. Yanmin Sun, Mohamed S. Kamel and Yang Wang, “Boosting for Learning Multiple
Classes with Imbalanced Class Distribution”, The Sixth International Conference on Data
Mining (ICDM’06).
Refrences