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Enhanced Fuzzy Roughset based Feature Selection Strategy using Differential Evolution

Dimensionality reduction technique aims to enhance the performance of the classification model by removing the irrelevant and redundant data. Reducing the number of attributes in the classification model helps to alleviate the curse of dimensionality, where it is the major crisis to data storage and retrieval [1].It also facilitates the mining techniques like classification, clustering, communication, visualization and high-dimensional data storage. Once the dimensionality of an object increases then it leads the data to sparsity. Sparsity problem is quite tricky for any technique that entails statistical importance. Thus, it is necessary to remove the irrelevant and redundant features, augment the performance of the classifier model, speed up the learning task, interpret the model and highlight the vital features with their relations. In this paper, a modified feature selection technique based on fuzzy Roughset theory and Differential Evolution is proposed. Here, the experimental results are carried out using binary and multiclass datasets taken from UCI Machine Learning Repository.