Learning generalized weighted relevance aggregation operators using levenberg-marquardt method

B. S.U. Mendis, T. D. Gedeon, L. T. Kóczy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006
DOIs
Publication statusPublished - Dec 1 2006
Event6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006 - Auckland, New Zealand
Duration: Dec 13 2006Dec 15 2006

Publication series

NameProceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006

Other

Other6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006
CountryNew Zealand
CityAuckland
Period12/13/0612/15/06

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Mendis, B. S. U., Gedeon, T. D., & Kóczy, L. T. (2006). Learning generalized weighted relevance aggregation operators using levenberg-marquardt method. In Proceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006 [4041414] (Proceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006). https://doi.org/10.1109/HIS.2006.264917