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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
Machine learning in virtual screening.
TL;DR: This review highlights recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target.
Journal ArticleDOI
Can traditional fault prediction models be used for vulnerability prediction
Yonghee Shin,Laurie Williams +1 more
TL;DR: The results suggest that fault prediction models based upon traditional metrics can substitute for specialized vulnerability prediction models, however, both fault prediction andulnerability prediction models require significant improvement to reduce false positives while providing high recall.
Proceedings ArticleDOI
Learning to log: helping developers make informed logging decisions
TL;DR: This paper provides the design and implementation of a logging suggestion tool, Log Advisor, which automatically learns the common logging practices on where to log from existing logging instances and further leverages them for actionable suggestions to developers.
Journal ArticleDOI
Skin lesion image segmentation using Delaunay Triangulation for melanoma detection.
Andrea Pennisi,Domenico Daniele Bloisi,Daniele Nardi,Anna Rita Giampetruzzi,Chiara Mondino,Antonio Facchiano +5 more
TL;DR: A fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented, using Delaunay Triangulation to extract a binary mask of the lesion region, without the need of any training stage.
Journal ArticleDOI
New rule-based phishing detection method
TL;DR: A new rule-based method to detect phishing attacks in internet banking is presented by extracting the hidden knowledge from the proposed SVM model by adopting a related method and embedded the extracted rules into a browser extension named PhishDetector.
References
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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.