DIMACS TR: 2004-02

A Study of K-Means Clustering for Improving Classification Accuracy of Multi-Class SVM

Authors: Dmitriy Fradkin and Ilya Muchnik


This work discusses how clustering methods, in particular K-Means, can be used to improve classification accuracy. We discuss two approaches to constructing hierarchical classifiers using cluster analysis and suggest new methods and improvements in each of these approaches. We also suggest a new method for constructing features that improve classification accuracy. All the methods are evaluated in the context of multi-class classification problems using multi-class SVM and some standard datasets from UCI.

Paper Available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2004/2004-02.ps.gz
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