Decision Support System Using Fuzzy Min-Max Neural Network with the Modified Genetic Algorithm

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Pattern recognition and classification has been an active research area for many decades and has got a surge in the recent past. The decision support system based on this has been explored in many areas including the business and market research. A two-phase pattern classification and rule extraction system is proposed using fuzzy min-max neural network and modified genetic algorithm in this paper. The first phase consists of a modified fuzzy min–max (FMM) neural-network-based pattern classifier, while the second phase consists of a genetic-algorithm (GA)-based rule extractor. We have redesigned the pruning equation (confidence factor) in the later part of first stage and the rule extraction is carried out using proposed Oppositional Genetic Algorithm with gene amplification operator. The experimentation is carried out using Tea dataset and Glass dataset. The evaluation metric used is the accuracy and we have also compared to existing methods. From the results we can see that our proposed technique has achieved better accuracy values
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Fuzzy Min–Max (FMM) Neural Network; Confidence Factor; Genetic Algorithm; Pattern Classification; Rule Extraction

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