Abstract
According to characteristics of new problems, the process of finding one or more similar cases from the existing cases to get a new solution is called case-based reasoning (CBR). The kernel idea of CBR is similar in cases having similar solutions. CBR can play its best role only by finding cases that are most similar to new problems through some retrieval methods. Currently, commonly used case retrieval algorithms are basically based on mean operator method. Although the difficulty of calculation is low, the accuracy is limited, and if a certain local similarity is low, the overall result can be affected. We introduce the soft likelihood functions into case retrieval, combine it with KNN, and propose a hybrid retrieval strategy, which is a new and softer way to calculate case similarity. The core of our hybrid retrieval strategy is to aggregate the local similarity and feature similarity of cases by soft likelihood functions, so as to obtain the global similarity. And at the same time, take into account the different attitudinal characteristics of the decision-maker, whether optimistic or pessimistic. The accuracy of this strategy is more than 81\(\%\) in simulation experiments on real data sets, which verifies its effectiveness.
Highlights
The proposal of case-based reasoning (CBR) can be traced back to the late 1970s [1]
The retrieval strategy we proposed is to combine the case retrieval algorithm based on soft likelihood functions developed above with K-nearest neighbor (KNN), replacing the traditional KNN strategy combined with the ordinary mean algorithm or the weight average method, so as to improve the accuracy of case retrieval in CBR
We introduce the soft likelihood functions based on ordered weighted average (OWA) operator into case-based reasoning, and propose a retrieval strategy based on case-based reasoning process
Summary
The proposal of case-based reasoning (CBR) can be traced back to the late 1970s [1]. Roger et al from Yale University in the United States proposed to represent knowledge by means of script, which is regarded as the beginning of CBR research. The basic idea of case retrieval by the proposed method is as follows: Firstly, calculate 70 the local similarity between different attributes of the target case and the source case; the CBR-SLFs algorithm proposed in this paper is used to calculate the overall similarity, and some potential available source cases with high similarity are obtained; the source case solution that is closest to the target case is obtained through KNN, and reuse it This strategy is developed as a flexible computation of likelihood functions of global similarity calculation, and has the advantage 75 of being more robust and practical in case retrieval [41].
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