Feature selection methods have been used in various applications of machine learning, bioinformatics, pattern recognition and network traffic analysis. In high dimensional datasets, due to redundant features and curse of dimensionality, a learning method takes significant amount of time and performance of the model decreases. To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features. But, most feature selection methods are unstable in nature, i.e., for different training datasets, a feature selection method selects different subsets of features that yields different classification accuracy. In this paper, we provide an ensemble feature selection method using feature–class and feature-feature mutual information to select an optimal subset of features by combining multiple subsets of features. The method is validated using four classifiers viz., decision trees, random forests, KNN and SVM on fourteen UCI, five gene expression and two network datasets.
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