An integrated feature selection and cluster analysis techniques for case-based reasoning. (March 2015)

• Record Type: Journal Article
• Authors: Zhu, Guo-Niu; Hu, Jie; Qi, Jin; Ma, Jin; Peng, Ying-Hong
• Is Part Of: Engineering applications of artificial intelligence. Volume 39(2015:Mar.)
• Page Start: 14
• Page End: 22
• Publication Date: 2015-03
• Subjects: Case-based reasoning -- Feature selection -- Cluster analysis -- Case organization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2014.11.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Feature selection and case organization are crucial steps in case-based reasoning (CBR), since the retrieval efficiency and accuracy even the success of the CBR system are heavily dependent on their quality. However, inappropriate feature selection and case selection together with ill-structured case organization may not only present a dilemma in case retrieval, but also greatly increase the case base. To obtain an efficient CBR system, selection of proper features and suitable cases with appropriate case organization are very important. This paper proposes a hybrid CBR system by introducing reduction technique in feature selection and cluster analysis in case organization. In this study, a minimal set of features is selected from the problem domain while redundant ones are reduced through neighborhood rough set algorithm. Once feature selection is finished, the growing hierarchical self-organizing map (GHSOM) is taken as a cluster tool to organize those cases so that the initial case base can be divided into some small subsets with hierarchical structure. New case is led into corresponding subset for case retrieval. Experiments on UCI datasets and a practical case in electromotor product design show the effectiveness of the proposed approach. The results indicate that the research techniques can effectively enhance the performance of the CBR system. Abstract : Highlights: A hybrid CBR system combined with feature selection and cluster analysis is proposed. Reduction approach is introduced in feature selection to reduce redundant features. Cluster analysis algorithm is presented in case organization to partition initial case base into small subsets for further retrieval. A computer-aided platform is developed based on the proposed method.