Squeezing the last drop: Cluster-based classification algorithm

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1288-1299
Number of pages12
JournalStatistics and Probability Letters
Issue number12
StatePublished - Jul 1 2007


  • Classification
  • Clustering
  • Feature selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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