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BFC: Bat Algorithm Based Fuzzy Classifier for Medical Data Classification

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Optimization algorithms are applied on Fuzzy system for various purposes like membership function optimization, co-efficient optimization, rule generation, rule selection, etc. Here we amalgamate Bat algorithm with fuzzy classifier to generate optimized rules and membership functions effectively. The key contributions in our classifier are (i) generating and selecting optimized rules using bat algorithm, (ii) simplifying the design and discretizing process in membership function, (iii) formulating a fitness function based on frequency of occurrence of the rules in the learning data. The proposed Bat algorithm based fuzzy classifier is subjected to quantitative and qualitative analysis for performance comparisons. Experimental results demonstrate that BFC has achieved 75.21% accuracy when Lung cancer data is used. Moreover, BFC has accomplished 76.67% accuracy for Indian Liver data.
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Keywords: BAT ALGORITHM; CLASSIFICATION; FREQUENCY; FUZZY SET; LOUDNESS; RULE OPTIMIZATION; VIRTUAL BATS

Document Type: Research Article

Publication date: June 1, 2015

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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