Hybrid Technique and Competence Preserving Case Deletion Methods for Case Maintenance in Case-Based Reasoning

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 after few runs. This paper proposes two case maintenance methods; the first method is Hybrid technique which combines case addition strategy and the footprint deletion and footprint utility deletion strategy and the second is competence-preserving case deletion technique which is consisted of four steps: determine a set of target problems, determine a candidate of cases , determine target problem and its candidate, delete less relevant cases.