Implications of Ceiling Effects in Defect Predictors
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This document discusses using less training data for defect prediction models. It finds that simple learners like Naive Bayes can achieve good performance using only small samples of data, and that oversampling and undersampling techniques do not significantly harm classifier performance. The document advocates increasing the information content in data rather than using more complex learners or larger datasets to further improve predictions.
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This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.
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Towards explanations for Data-Centric AI using provenance records
In this presentation, given to graduate students at Universita' RomaTre, Italy, we suggest that concepts well-known in Data Provenance can be exploited to provide explanations in the context of data-centric AI processes. Through use cases (incremental data cleaning, training set pruning), we build up increasingly complex provenance patterns, culminating in an open question:
how to describe "why" a specific data item has been manipulated as part of data processing, when such processing may consist of a complex data transformation algorithm.
2. visualization in data mining
Visual data mining combines traditional data mining methods with information visualization techniques to explore large datasets. There are three levels of integration between visualization and automated mining methods - no/limited integration, loose integration where methods are applied sequentially, and full integration where methods are applied in parallel. Different visualization methods exist for univariate, bivariate and multivariate data based on the type and dimensions of the data. The document describes frameworks and algorithms for visual data mining, including developing new algorithms interactively through a visual interface. It also summarizes a document on using data mining and visualization techniques for selective visualization of large spatial datasets.
Few shot learning/ one shot learning/ machine learning
The document discusses few-shot learning approaches. It begins with an introduction explaining that current deep learning models require large datasets but humans can learn from just a few examples. It then discusses the problem of few-shot learning, where models must perform classification, detection, or regression on novel categories represented by only a few samples. Popular approaches discussed include meta-learning methods like MAML and prototypical networks, metric learning methods like relation networks, and data augmentation methods. The document provides an overview of the goals and techniques of few-shot learning.
Big Data Challenges and Solutions
Big Data Challenges and SolutionsNew York City College of Technology Computer Systems Technology Colloquium
The document discusses big data challenges and potential solutions. It begins by outlining how big data is generated from various sources and used in applications like search engines. The main challenges are determining which subset of big data to analyze and how to clean noisy data. Two potential solutions discussed are:
1) Intelligent sampling to determine a representative subset of data to analyze instead of the entire dataset, in order to improve running time. Adaptive sampling techniques like IDASA are proposed.
2) Filtering techniques like ensemble filtering use multiple models to identify and remove mislabeled instances from training data, in order to improve predictive accuracy by cleaning the data. Bayesian analysis can interpret filtering as a form of model averaging.IDA 2015: Efficient model selection for regularized classification by exploit...
A new method for model selection. It is an alternative on standard methods such as k-fold cross validation and hold out. It build on a learning theory result, which is an upper bound on classification performance.
In the method, we use unlabeled data and quantification to accelerate the tuning of machine learning model hyper-parameters such as the C value of Support Vector Machines or Logistic Regression. We present classification results with data from Wikipedia and Dmoz with SVMs and Logistic Regression.
The method was presented as a paper on the Intelligent Data Analysis (IDA) 2015 conference.
DMDW Lesson 05 + 06 + 07 - Data Mining Applied
This document summarizes an application of data mining techniques to analyze customer data. It discusses using decision trees to model customer response to marketing campaigns. Decision trees partition customers into groups based on attributes like income and age to predict their response rates to mailings. Groups with a response rate over 3.5% would be targeted for direct marketing. Decision trees provide a flexible yet simple model for segmentation and targeting of customers.
Unit 3.pptx
This document discusses frequent pattern mining and association rule learning. It begins by defining frequent patterns as patterns that occur frequently in a dataset. Apriori and FP-Growth are introduced as two popular algorithms for mining frequent itemsets and generating association rules. The document then provides more details on the concepts and implementation of these two algorithms. It explains how Apriori uses a generate-and-test approach with candidate generation while FP-Growth adopts a pattern growth method to avoid candidate generation. Examples are also given to illustrate how each algorithm works step-by-step.
A data science observatory based on RAMP - rapid analytics and model prototyping
RAMP approach to analytics: Rapid Analytics and Model Prototyping; collaborative data challenges with in-built data science process management tools and analytics; An observatory of data science and scientists. Presented at the Design Theory Special Interest Group of International Design Society. Mines ParisTech and Centre for Data Science.
Lecture 2 Data mining process.pdf
This document outlines the typical phases in a data mining process: 1) discovery, 2) data preparation, 3) data exploration and conditioning, 4) model planning, 5) model building, 6) results interpretation, and 7) model deployment. It also discusses key aspects of each phase such as identifying business problems, obtaining and checking data, handling missing values, selecting modeling techniques, evaluating models, and deploying results. Supervised and unsupervised learning methods are introduced along with examples like prediction, classification, clustering and association rules.
UNIT-II-Machine-Learning.pptx Machine Learning Different AI Models
Machine Learning PPT about AI and ML Models. Includes Supervised and Unsupervised Learning along with Classifcation models
Bangla Hand Written Digit Recognition presentation slide .pptx
This document describes a project on Bangla handwritten digit recognition using deep learning models. It discusses preprocessing a dataset of 2500 training and 500 testing Bangla handwritten digit images. Two models - EfficientNetB0 and MobileNet - were trained using baseline, transfer learning, and fine-tuning methods. Fine-tuning achieved the best results, with 99.4% and 94% accuracy for EfficientNetB0 and MobileNet respectively. Limitations and future work are discussed to improve dataset quality and model performance.
Presentation on supervised learning
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.
Overfitting.pptx
The document discusses model overfitting in machine learning. It explains that overfitting occurs when a model learns the details and noise in the training data too well, reducing its ability to fit new data. Decision trees can overfit by becoming too complex, and increasing the size of the training data or using regularization techniques like pruning can help address overfitting. The document also covers techniques for evaluating models like cross-validation to estimate generalization performance on new examples.
Data mining , Knowledge Discovery Process, Classification
The document provides an overview of data mining techniques and processes. It discusses data mining as the process of extracting knowledge from large amounts of data. It describes common data mining tasks like classification, regression, clustering, and association rule learning. It also outlines popular data mining processes like CRISP-DM and SEMMA that involve steps of business understanding, data preparation, modeling, evaluation and deployment. Decision trees are presented as a popular classification technique that uses a tree structure to split data into nodes and leaves to classify examples.
Current clustering techniques
Survey of current clustering techniques of big data analysis and with provides the algorithm of ELM k-mean algorithm in detail.
Epsrcws08 campbell kbm_01
This document discusses kernel-based machine learning methods. It covers several topics:
1) Different types of optimization problems for kernel methods, such as linear programming and non-linear programming.
2) Tasks beyond classification/regression like novelty detection, and different types of learning like passive and active learning.
3) Details on training support vector machines and other kernel machines, including decomposition methods and model selection.
4) Different types of kernels including string kernels, and combining kernels for data fusion.
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Towards explanations for Data-Centric AI using provenance records
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Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
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UNIT-II-Machine-Learning.pptx Machine Learning Different AI Models
UNIT-II-Machine-Learning.pptx Machine Learning Different AI Models
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The document presents research on finding robust solutions to requirements models. It introduces the KEYS2 algorithm, which incrementally sets mitigation variables to balance solution quality and robustness. KEYS2 is faster than other algorithms like simulated annealing and A* search. It generates well-behaved results that allow exploring the decision neighborhood through decision ordering diagrams to assess robustness. The researcher concludes that KEYS2 is well-suited for optimizing requirements models as it is fast, produces stable solutions, and enables robustness analysis.
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This document reviews algorithms for distributed decision tree induction on large datasets. It discusses classifying data using decision trees and the need for parallelization due to big data. Three paradigms for parallelization are described: data parallelism by horizontally or vertically fragmenting data, task parallelism by splitting tree construction among processors, and hybrid approaches. Specific algorithms like C4.5 Parallel, SMP Tree Classifier, and INDUS are outlined that implement these paradigms. The algorithms show promising improvement over standard centralized methods.
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The document discusses optimizing requirements models using a new algorithm called KEYS. KEYS beats other standard algorithms like simulated annealing and state of the art methods. KEYS allows requirements models to be optimized 50,000 times faster, in almost real-time. KEYS works by first identifying "keys" or important variables that influence the overall model, and then optimizing settings for those key variables. The document provides details on how KEYS finds keys using a technique called BORE sampling. It also discusses using KEYS to optimize a requirements model called DDP used at JPL for designing deep space missions.
Promise08 Wrapup
The document discusses the PROMISE community and ways its members can collaborate more effectively. It describes several case studies where collaboration between PROMISE researchers led to improved results, new data, and funding opportunities. It then proposes several online tools like chat rooms, discussion forums, and code libraries to further foster collaboration within the PROMISE community between annual meetings.
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Risk And Relevance 20080414ppt
The document discusses how software provides value for other industries and the challenges of efficiently delivering that value. While estimates of costs and schedules are often inaccurate, focusing only on avoiding overruns limits opportunities. Instead, engineering principles can be applied to create value by treating costs and benefits as random variables, reducing uncertainty over time, and increasing potential upside benefits. Tailoring governance approaches based on risk areas can improve value delivery.
Organizations Use Data
- This document discusses how data mining techniques have been successfully applied to various NASA data sources to predict software quality metrics like effort and defects.
- However, despite clear success, most of the data sources used in these examples have been discontinued or are no longer accessible.
- The main challenge is organizational rather than technological - how can we ensure the valuable lessons learned from this work are not wasted?
Cukic Promise08 V3
The study compared software quality prediction models using design metrics, code metrics, and a combination of both, on multiple projects from NASA. The results showed that models using a combination of design and code metrics performed better than using either metric set alone, and code metrics generally performed better than design metrics alone. Combining metrics from different development phases provided better fault prediction than only using code-level metrics.
Boetticher Presentation Promise 2008v2
The document discusses building human-based software estimation models that are accurate, intuitive, and easy to understand. It presents an approach using correlation and scale factors between estimated and actual effort. Experiments on a dataset of 178 samples show that combining correlation and scale factors into a decision tree achieves up to 93.3% accuracy. The resulting model bridges expert and algorithmic estimation methods.
Elane - Promise08
The document compares two statistical modeling techniques, negative binomial regression and recursive partitioning, for predicting fault-prone files in large software systems. It finds that recursive partitioning more accurately identifies the top 20% most fault-prone files, correctly predicting 67.9-93.4% of faults, compared to 75-83% for negative binomial regression. Recursive partitioning thus appears better able to help prioritize testing and identify files for rearchitecting or additional verification.
Risk And Relevance 20080414ppt
The document discusses how software provides value for many industries and the economy. However, projects often experience cost overruns, delays, and failures to deliver value efficiently. The document argues that development costs, schedules, and values are uncertain and best viewed as ranges rather than single estimates. Seeing projects this way allows focusing on continuously improving estimates and reducing risks to better ensure delivery of value over the project lifecycle. Tailoring governance approaches to different project types based on their risks and opportunities for discovery can help optimize value creation.
Introduction Promise 2008 V3
This document summarizes the 4th International Predictor Models in Software Engineering (PROMISE) Workshop held on May 12-13, 2008. The workshop focused on bridging industry and research in software engineering. It included keynote speakers from industry, papers on topics like defect prediction, effort estimation, and data quality, and discussions on how to better align research with industry needs and validate models for practical use.
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