Application of Information Gain Based Weighted LVQ for Heart Disease Diagnosis
Keywords:
LVQ, IG, IG-WLVQ, MLAbstract
To enhance the performance of Linear Vector Quantization (LVQ) for classification, an Information Gain based Weighted Linear Vector Quantization (IG-WLVQ) method is proposed in this paper. The information gain technique performs feature selection and provides informative attributes. So, information gain concept is embedded in winning vector calculation of the existing LVQ technique. It does dynamic features selection as well as calculates the winning vector in the run time. For analysing the performance of IG-WLVQ, Cleveland Heart disease dataset from UCI machine learning repository is used. Thus, the attributes having zero information are automatically wiped out in learning, and winning vectors are calculated from informative attributes. IG-WLVQ method not only performs dynamically future selection but also improves the classification performance of LVQ with a classification accuracy reaches to 100%.
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