Abstract
Case-based reasoning (CBR) is a problem solving technique that uses previous experiences to solve new problems. Among the four phases of CBR, Retrieval is the first and the most important phase, as it lays the foundation of the entire CBR cycle. Retrieval aims to retrieve similar cases from the case-base, given a new situation. CBR systems typically use a strategy called similarity-based retrieval for retrieving cases. One of the derivatives of similarity-based retrieval is k-nearest neighbor (k-NN) algorithm. In this paper, we compare the performances of k-NN, Fuzzy nearest neighbor (Fuzzy NN) and Genetic Programming (GP) classifiers for retrieval of cases. We evaluate these algorithms in WEKA, with benchmark data sets for classification from UCI.
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