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Various feature selection techniques have been proposed in the field of machine learning. The filter approaches are typically faster while wrapper approaches are more reliable though computationally expensive. Feature selection techniques often strive to achieve performance similar to wrapper approaches employing various computational approaches. Feature selection techniques typically depend on ways how they compute feature–feature correlation and feature–class correlation. These two computations are highly governed by the correlation measure being used. In this work, a method is developed named enhanced correlation-based feature selection (ECFS) to effectively employ the feature–feature and feature–class correlations to extract relevant feature subset from multi-class gene expression data as well as machine learning datasets. The performance of ECFS in terms of classification accuracies obtained by decision tree, random forest and KNN classifiers has been found highly satisfactory over several benchmark datasets.