Case-based reasoning classifier based on learning pseudo metric retrieval. (15th December 2017)

• Record Type: Journal Article
• Authors: Yan, Aijun; Yu, Hang; Wang, Dianhui
• Is Part Of: Expert systems with applications. Volume 89(2017)
• Page Start: 91
• Page End: 98
• Publication Date: 2017-12-15
• Subjects: Case-based reasoning -- Learning pseudo metric -- Case retrieval -- Case reuse -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.07.022
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A learning pseudo metric case-based reasoning classification method is proposed. A new classifier is redesigned on the basis of the traditional CBR classifier. The algorithm of LPM-CBR has some anti-interference ability and good robustness. The LPM-based retrieval method can improve the quality and learning ability of CBR. Abstract: In case-based reasoning (CBR) classification systems, the similarity metrics play a key role and directly affect the system's performance. Based on our previous work on the learning pseudo metrics (LPM), we propose a case-based reasoning method for pattern classification, where the widely used Euclidean distance is replaced by the LPM to measure the closeness between the target case and each source case. The same type of case as the target case can be retrieved and the category of the target case can be defined by using the majority of reuse principle. Experimental results over some benchmark datasets and a fault diagnosis of the Tennessee-Eastman (TE) process demonstrate that the proposed reasoning techniques in this paper can effectively improve the classification accuracy, and the LPM-based retrieval method can substantially improve the quality and learning ability of CBR classifiers.