Using conjunction of attribute values for classification

Mukund Deshpande, George Karypis

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

22 Scopus citations

Abstract

Advances in the efficient discovery of frequent itemsets have led to the development of a number of schemes that use frequent itemsets to aid developing accurate and efficient classifiers. These approaches use the frequent itemsets to generate a set of composite features that expand the dimensionality of the underlying dataset. In this paper, we build upon this work and (i) present a variety of schemes for composite feature selection that achieve a substantial reduction in the number of features without adversely affecting the accuracy gains, and (ii) show (both analytically and experimentally) that the composite features can lead to improved classification models even in the context of support vector machines, in which the dimensionality can automatically be expanded by the use of appropriate kernel functions.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
EditorsK Kalpakis, N Goharian, D Grossman
Pages356-364
Number of pages9
StatePublished - Dec 1 2002
EventProceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002) - McLean, VA, United States
Duration: Nov 4 2002Nov 9 2002

Other

OtherProceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002)
CountryUnited States
CityMcLean, VA
Period11/4/0211/9/02

Keywords

  • Association rules
  • Classification
  • Conjunctive attributes
  • Feature selection
  • SVM

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