Visual methods for examining SVM classifiers

Doina Caragea, Dianne Cook, Hadley Wickham, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Support vector machines (SVM) offer a theoretically wellfounded approach to automated learning of pattern classifiers. They have been proven to give highly accurate results in complex classification problems, for example, gene expression analysis. The SVM algorithm is also quite intuitive with a few inputs to vary in the fitting process and several outputs that are interesting to study. For many data mining tasks (e.g., cancer prediction) finding classifiers with good predictive accuracy is important, but understanding the classifier is equally important. By studying the classifier outputs we may be able to produce a simpler classifier, learn which variables are the important discriminators between classes, and find the samples that are problematic to the classification. Visual methods for exploratory data analysis can help us to study the outputs and complement automated classification algorithms in data mining. We present the use of tour-based methods to plot aspects of the SVM classifier. This approach provides insights about the cluster structure in the data, the nature of boundaries between clusters, and problematic outliers. Furthermore, tours can be used to assess the variable importance. We show how visual methods can be used as a complement to crossvalidation methods in order to find good SVM input parameters for a particular data set.

Original languageEnglish (US)
Title of host publicationVisual Data Mining - Theory, Techniques and Tools for Visual Analytics
EditorsSimeon J. Simoff, Michael H. Bohlen, Arturas Mazeika
Pages136-153
Number of pages18
DOIs
StatePublished - Aug 29 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4404 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Caragea, D., Cook, D., Wickham, H., & Honavar, V. (2008). Visual methods for examining SVM classifiers. In S. J. Simoff, M. H. Bohlen, & A. Mazeika (Eds.), Visual Data Mining - Theory, Techniques and Tools for Visual Analytics (pp. 136-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4404 LNCS). https://doi.org/10.1007/978-3-540-71080-6_10