ビジネス問題に適用可能な記憶に基づく推論

抄録

Recently, data mining is remarkable as a practical solution for huge accumulated data. The classification, the goal of which is that a new data is classified into one of given groups, is one of the most generally used data mining techniques. In this paper, we discuss advantages of Memory-Based Reasoning (MBR), one of classification methods, and point out some problems to use it practically. To solve them, we propose a MBR applicable to business problems, with self-determination of proper number of neighbors, proper feature weights, normalized distance metric between categorical values, high accuracy despite dependent features, and high speed prediction. We experimentally compare our MBR with usual MBR and C5.0, one of the most popular classification methods. We also discuss the fitness of our MBR to business problems, through an application study of our MBR to the financial credit management.