Authors - Ridhi Sharma, Ashok Kumar Abstract - This manuscript discovers the role of information theoretic measures for feature selection while dealing with high dimensional data sets. The study uses entropy, mutual information and divergence measures to address the issues of classification and high computational complexity of real data set which is affect by redundant and irrelevant features, by analyzing the dependency patterns and feature relevance in complex data set. Under different data conditions, the proposed approach for feature selection, in comparison to traditional methods, handles the non-linear relationships and noisy attributes effectively in terms of relevance, classification and interpretation. In-formation theoretic methods provide more precise feature selection and pattern identification results in the data sets. Despite the challenges of computational cost and scalability, the study shows that information theoretic measures can perform better in feature selection and decision making of the data mining.
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