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Squeezing the last drop: Cluster-based classification algorithm

Author

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  • Mehrotra, Kishan G.
  • Ozgencil, Necati E.
  • McCracken, Nancy

Abstract

In this paper we propose a new approach for classification problems and apply it to eight problems. A classification problem with a large feature set is partitioned using clustering on a subset of the features. A multi-label classifier is then trained individually on each cluster, using automatic feature selection to customize the feature set for the cluster. The algorithm achieves one to two percent higher accuracy for most of the problems investigated in this study.

Suggested Citation

  • Mehrotra, Kishan G. & Ozgencil, Necati E. & McCracken, Nancy, 2007. "Squeezing the last drop: Cluster-based classification algorithm," Statistics & Probability Letters, Elsevier, vol. 77(12), pages 1288-1299, July.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:12:p:1288-1299
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    Cited by:

    1. Gupta, Ankit & Mehrotra, Kishan G. & Mohan, Chilukuri, 2010. "A clustering-based discretization for supervised learning," Statistics & Probability Letters, Elsevier, vol. 80(9-10), pages 816-824, May.

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