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.
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