英文标题
An Improved Classification Approach by Case-based Reasoning
英文摘要
The distribution of feature attribute weights and the strategy of case retrieval have significant impacts on the classification accuracy of case-based reasoning (CBR).An improved CBR classification approach is proposed,which is combined with genetic algorithms,introspective learning,and group decision-making theory.First,multiple attribute weights are given by a genetic algorithm.Then each group of weights is iteratively adjusted in accordance with the introspective learning principle.After that the group decision-making retrieval result which satisfies the plurality rule can be obtained according to the case group-retrieval strategy.At last,the classification comparison experiments prove that the proposed method could improve the classification accuracy of CBR.The results indicate that the introspective learning could guarantee the rationality of weight allocation,and that the case group-retrieval strategy could make full use of the potential knowledge of case base,having remarkable effects on promoting the learning ability of CBR.
翻译关键词
Case-based reasoning (CBR)
case retrieval
genetic algorithms
introspective learning
group decision-making
