Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method
Date
2006
Authors
Mendis, B Sumudu
Gedeon, Tamas (Tom)
Koczy, Lazlo
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Institute of Electrical and Electronics Engineers (IEEE Inc)
Abstract
We previously introduced the generalized Weighted Relevance Aggregation Operators (WRAO) for hierarchical fuzzy signatures. WRAO enhances the ability of the fuzzy signature model to adapt to different applications and simplifies the learning of fuzzy signature models from data. In this paper we overcome the practical issues which occur when learning WRAO from data. This paper discuss an algorithm for learning WRAO using the Levenberg-Marquardt (LM) method, which is one of the most sophisticated and widely used gradient based optimization method. Also, this paper shows the successful results of applying the proposed algorithm to extract WRAO for two real world problems namely High Salary Selection and SARS Patient Classification.
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Keywords
Keywords: Algorithms; Fuzzy sets; Hierarchical systems; Fuzzy signatures; Levenberg-Marquardt (LM) method; Real world problems; Mathematical operators
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Source
Proceedings of International Conference on Hybrid Intelligence Systems (HIS 2006)
Type
Conference paper
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2037-12-31
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