Integrating statistical and inductive learning methods for knowledge acquisition

Ting Peng Liang, John S. Chandler, Ingoo Han

Research output: Contribution to journalArticlepeer-review

Abstract

Inductive learning is a method for automated knowledge acquisition. It converts a set of training data into a knowledge structure. In the process of knowledge induction, statistical techniques can play a major role in improving performance. In this paper, we investigate the competition and integration between the traditional statistical and the inductive learning methods. First, the competition between these two approaches is examined. Then, a general framework for integrating these two approaches is presented. This framework suggests three possible integrations: (1) statistical methods as preprocessors for inductive learning, (2) inductive learning methods as preprocessors for statistical classification, and (3) the combination of the two methods to develop new algorithms. Finally, empirical evidence concerning these three possible integrations are discussed. The general conclusion is that algorithms integrating statistical and inductive learning concepts are likely to make the most improvement in performance.

Original languageEnglish (US)
Pages (from-to)391-401
Number of pages11
JournalExpert Systems With Applications
Volume1
Issue number4
DOIs
StatePublished - 1990

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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