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Neuro fuzzy classification and detection technique for bioinformatics problems

Othman, Mohd. Fauzi and Moh, Thomas Shan Yau (2007) Neuro fuzzy classification and detection technique for bioinformatics problems. In: Proc International Conference on Asia Modelling and Simulation Symposium (AMS2007), 13-16 Mac 2007, Phuket, Thailand.

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Bioinformatics is an emerging science and technology which has lots of research potential in the future. It involves multi-interdisciplinary approaches such as mathematics, physics, computer science and engineering, biology, and behavioral science. Computers are used to gather, store, analyze as well as integration of patterns and biological data information which can then be applied to discover new useful diagnosis or information. In this study, the focus was directed to the classification or clustering techniques which can be applied in the bioinformatics fields based on the Sugeno type neuro fuzzy model or ANFIS (adaptive neuro fuzzy inference system). It is very important to identify new integration of classification or clustering algorithm especially in neuro fuzzy domain as compared to conventional or traditional method. This paper explores the suitability and performance of recurrent classification technique, fuzzy c means (FCM) act as classifier in neuro fuzzy system compared to subclustering method. A package of software based on neuro fuzzy model (ANFIS) has been developed using MATLAB software and optimization were done with the help from WEKA. A set diabetes data based on real diagnosis of patient was used.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:24432
Deposited On:18 Apr 2012 05:40
Last Modified:14 Sep 2017 04:47

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