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Data Mining: Practical Machine Learning Tools and Techniques
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TLDR
This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.Abstract:
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualizationread more
Citations
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Proceedings ArticleDOI
Properties of Machine Learning Applications for Use in Metamorphic Testing
TL;DR: This work seeks to enumerate and classify the metamorphic properties of some machine learning algorithms, and demonstrates how these can be applied to reveal defects in the applications of interest.
Book ChapterDOI
A comparison of sentiment analysis techniques: polarizing movie blogs
Michelle Annett,Grzegorz Kondrak +1 more
TL;DR: A novel approach based on Support Vector Machines is proposed to classify a subset of documents using polarity metrics by applying it to a publicly available set of movie reviews.
Journal ArticleDOI
Facing classification problems with Particle Swarm Optimization
TL;DR: Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases and turns out to be the best on 3 out of the 13 challenged problems.
Journal ArticleDOI
A comparative study on base classifiers in ensemble methods for credit scoring
TL;DR: It is shown that a very simple base classifier, based on imprecise probabilities and uncertainty measures, attains a better trade-off among some aspects of interest for this type of studies such as accuracy and area under ROC curve (AUC).
Proceedings Article
The 2007 MIREX Audio Mood Classification Task: Lessons Learned
TL;DR: Important issues in setting up the AMC task are described, dataset construction and ground-truth labeling are analyzed, and human assessments on the audio dataset, as well as system performances from various angles are analyzed.
References
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Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.