X2R: a fast rule generator

Huan Liu, Sun Teck Tan

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

38 Scopus citations

Abstract

Although they can learn from raw data, many concept learning algorithms require that the training data contain only discrete data. However, real world problems contain, more often than not, both numeric and discrete data. So before these algorithms can be applied, data discretization (quantization) is needed. This paper introduces X2R, a simple and fast algorithm that can be applied to both numeric and discrete data, and generate rules from datasets like Season-Classification, Golf-Playing that contain continuous and/or discrete data. The empirical results demonstrate that X2R can effectively generate rules from the raw data and perform better than some of its peers in terms of the quality of rules and time complexities.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Editors Anon
PublisherIEEE
Pages1631-1635
Number of pages5
Volume2
StatePublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can
Duration: Oct 22 1995Oct 25 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5)
CityVancouver, BC, Can
Period10/22/9510/25/95

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

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  • Cite this

    Liu, H., & Tan, S. T. (1995). X2R: a fast rule generator. In Anon (Ed.), Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 2, pp. 1631-1635). IEEE.