Nathalie Japkowicz

Evaluating Learning Algorithms (eBook, PDF)

A Classification Perspective

• Format: PDF

Produktbeschreibung

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 17. Januar 2011
• Englisch
• ISBN-13: 9781139065009
• Artikelnr.: 38276383

Autorenporträt

Nathalie Japkowicz is Professor of Computer Science at American University. She is a former assistant professor at Dalhousie University and lecturer at Ohio State University. Japkowicz co-organized numerous workshops on classifier evaluation and the class imbalance problem at AAAI and ICML. She has published many articles in peer-reviewed journals and conference proceedings.

Inhaltsangabe

1. Introduction
2. Machine learning and statistics overview
3. Performance measures I
4. Performance measures II
5. Error estimation
6. Statistical significance testing
7. Data sets and experimental framework
8. Recent developments
9. Conclusion
Appendix A: statistical tables
Appendix B: additional information on the data
Appendix C: two case studies.