A hybrid retrieval strategy for case-based reasoning using soft likelihood functions

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%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} in simulation experiments on real data sets, which verifies its effectiveness.