Fuzzy-based Threshold Controlling Method for ART1 Clustering in GPCR Classification
2007, 12(6), pp.167-176
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models. The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller. The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.
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