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Data Mining: Practical Machine Learning Tools and Techniques
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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
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Journal ArticleDOI
Exploratory Undersampling for Class-Imbalance Learning
TL;DR: Experiments show that the proposed algorithms, BalanceCascade and EasyEnsemble, have better AUC scores than many existing class-imbalance learning methods and have approximately the same training time as that of under-sampling, which trains significantly faster than other methods.
Journal ArticleDOI
Financial time series forecasting using support vector machines
TL;DR: The experimental results show that SVM provides a promising alternative to stock market prediction and the feasibility of applying SVM in financial forecasting is examined by comparing it with back-propagation neural networks and case-based reasoning.
Journal ArticleDOI
A survey of techniques for internet traffic classification using machine learning
TL;DR: This survey paper looks at emerging research into the application of Machine Learning techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques.
Journal ArticleDOI
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
TL;DR: This paper presents a new hybrid sampling/boosting algorithm, called RUSBoost, for learning from skewed training data, which provides a simpler and faster alternative to SMOTEBoost, which is another algorithm that combines boosting and data sampling.
Proceedings ArticleDOI
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more
TL;DR: The main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.
References
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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
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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.