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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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Journal ArticleDOI
A novel feature selection method considering feature interaction
TL;DR: The results on the eight real world datasets indicate that IWFS not only efficiently reduces the dimensionality of feature space, but also offers the highest average accuracy for all the three classification algorithms.
Proceedings ArticleDOI
Learning to recommend helpful hotel reviews
Michael P. O'Mahony,Barry Smyth +1 more
TL;DR: A classification-based recommender system that is designed to recommend the most helpful reviews for a given product, and it is shown that this approach is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks.
Journal ArticleDOI
Machine learning techniques and drug design.
Jadson Castro Gertrudes,Vinícius Gonçalves Maltarollo,Ricardo Silva,Patrícia R. Oliveira,Káthia Maria Honório,A. B. F. da Silva +5 more
TL;DR: A critical point of view on the main MLT shows their potential ability as a valuable tool in drug design and shows that MLT have significant advantages.
Journal ArticleDOI
Software defect prediction: do different classifiers find the same defects?
TL;DR: It is concluded that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.
Journal ArticleDOI
Survey of State-of-the-Art Mixed Data Clustering Algorithms
Amir Ahmad,Shehroz S. Khan +1 more
TL;DR: A taxonomy for the study of mixed data clustering algorithms by identifying five major research themes is presented in this article. But it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets.
References
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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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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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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.
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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.