Feature subset selection is a crucial phase in modeling accurate classifiers in data mining and machine learning, especially with High Dimensional Small Sized (HDSS) data. LDA can also be used for feature selection as an efficient measure for evaluation of the feature subset. While LDA is applied to feature selection on HDSS data and class imbalance, it meets some difficulties, such as singular scatter matrix, overwhelming, overfitting, and computational complexity. For this purpose, a new LDA based feature selection technique based is proposed which focuses more on minority class with a novel regularization technique. Main objective is to enhance the performance of feature subset selection process using LDA in distributed environment. Sample ratio between both classes has been determined.

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Feature Subset Selection Class Emphasis Hdss Classification Regularization