An N-version Learning Approach to Enhance the Prediction Accuracy of Classification Systems in Genetics-based Learning Envirornments


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 6, No. 7, pp. 1841-1848, Jul. 1999
10.3745/KIPSTE.1999.6.7.1841,   PDF Download:

Abstract

DELVAUX is a genetics-based inductive learning system that learns a rule-set, which consists of Bayesian classification rules, from sets of examples for classification tasks. One problem that DELVAUX faces in the rule-set learning process is that, occasionally, the learning process ends with a local optimum without finding the best rule-set. Another problem is that, occasionally, the learning process ends with a rule-set that performs well for the training examples but not for the unknown testing examples. This paper describes efforts to alleviate these two problems centering on the N-version learning approach, in which multiple rule-sets are learned and a classification system is constructed with those learned rule-sets to improve the overall performance of a classification system. For the implementation of the N-version learning approach, we propose a decision-making scheme that can draw a decision using multiple rule-sets and a genetic algorithm approach to find a good combination of rule-sets from a set of learned rule-sets. We also present empirical results that evaluate the effect of the N-version learning approach in the DELVAUX learning environment.


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Cite this article
[IEEE Style]
K. Y. Joon and H. C. Eui, "An N-version Learning Approach to Enhance the Prediction Accuracy of Classification Systems in Genetics-based Learning Envirornments," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 6, no. 7, pp. 1841-1848, 1999. DOI: 10.3745/KIPSTE.1999.6.7.1841.

[ACM Style]
Kim Yeong Joon and Hong Chul Eui. 1999. An N-version Learning Approach to Enhance the Prediction Accuracy of Classification Systems in Genetics-based Learning Envirornments. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 6, 7, (1999), 1841-1848. DOI: 10.3745/KIPSTE.1999.6.7.1841.