A rule induction algorithm for continuous data using analysis of variance

Ramesh Konda, K. P. Rajurkar

Research output: Contribution to journalConference article

Abstract

Knowledge acquisition continuous to be a challenging and time consuming task in building decision support systems. Among the dominant methods, rule induction algorithms such as ID3 and C4.5 are widely used to extract rules from examples. The thrust of these algorithms is how they discriminate the given attributes based on information measure for building and determining the nodes in decision tree. In particular, the main focus of these algorithms is on how to select the most appropriate attribute at each level of the decision tree process. This paper proposes an algorithm for rule induction for continuous data. The proposed algorithm uses an analysis of variance criterion for information measure in discriminating the given attributes for building the decision tree for continuous of data.

Original languageEnglish (US)
Pages (from-to)489-494
Number of pages6
JournalConference Proceedings - IEEE SOUTHEASTCON
StatePublished - Nov 9 2005
EventIEEE Southeastcon 2005: Excellence in Engineering, Science and Technology - Ft. Lauderdale, United Kingdom
Duration: Apr 8 2005Apr 10 2005

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Analysis of variance (ANOVA)
Decision trees
Knowledge acquisition
Decision support systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

A rule induction algorithm for continuous data using analysis of variance. / Konda, Ramesh; Rajurkar, K. P.

In: Conference Proceedings - IEEE SOUTHEASTCON, 09.11.2005, p. 489-494.

Research output: Contribution to journalConference article

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