Integrating statistical and inductive learning methods for knowledge acquisition

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. © 1990.
Publisher
Elsevier BV
Issue Date
1990
Language
ENG
Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.1, no.4, pp.391 - 401

ISSN
0957-4174
URI
http://hdl.handle.net/10203/65909
Appears in Collection
KGSF-Journal Papers(저널논문)
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