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Methods for Case Maintenance in Case-Based Reasoning
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BibTeX
@MISC{Lawanna_methodsfor,
author = {A. Lawanna and J. Daengdej},
title = {Methods for Case Maintenance in Case-Based Reasoning},
year = {}
}
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Abstract
Abstract—Case-Based Reasoning (CBR) is one of machine learning algorithms for problem solving and learning that caught a lot of attention over the last few years. In general, CBR is composed of four main phases: retrieve the most similar case or cases, reuse the case to solve the problem, revise or adapt the proposed solution, and retain the learned cases before returning them to the case base for learning purpose. Unfortunately, in many cases, this retain process causes the uncontrolled case base growth. The problem affects competence and performance of CBR systems. This paper proposes competence-based maintenance method based on deletion policy strategy for CBR. There are three main steps in this method. Step 1, formulate problems. Step 2, determine coverage and reachability set based on coverage value. Step 3, reduce case base size. The results obtained show that this proposed method performs better than the existing methods currently discussed in literature.
Keyphrases
case-based reasoning case maintenance competence-based maintenance method many case machine learning algorithm case base size abstract case-based reasoning cbr system retain process uncontrolled case base growth case base similar case problem solving learned case determine coverage last year coverage value main phase formulate problem main step deletion policy strategy
