关键词: 案例推理, 特征权值, 群决策方法, 粗糙集, 综合权值

Abstract: In the Case-Based Reasoning (CBR) case retrieving and matching, different cases are usually composed by different features. But most of the traditional CBR engines adopt fixed feature weights mode, which makes matching rate of whole system very low. To solve this problem, this paper proposed a CBR engine model with variable feature weights and brought interactive mode into feature weights calculating module. It calculated subjective weight based on group decisionmaking theory and proposed an adjustment method which used differences between a single expert and his group. It used similarity rough set theory to calculate objective weight in order to make results calculating more objective and accurate. At last, it designed composite weights adjustment algorithm which calculated the distance between the subjective weight and objective weight, considered the deviation degree of those two weights, then deduced weights adjustment coefficient, and get the final weight adjustment results. The calculation example and simulation analysis of network attack cases validate the effectiveness of the proposed method and prove this method has much better performance in different performance indexes.

Key words: case-based reasoning, feature weight, group decision-making theory, similarity rough set theory, synthetical weight