Fuzzy joint mutual information feature selection based on ideal vector

Feature selection is an effective preprocess that helps the classification task to improve its performance. Information measures, especially fuzzy information measures have been a powerful solution in feature selection. However, existing feature selection frameworks based on fuzzy information measures suffer from one of two limitations: (1) discarding important feature relations as redundancy and complementarity. (2) requiring a high computational cost. To overcome these limitations, a new feature selection framework is proposed in this paper, called fuzzy joint mutual information based on ideal vector (FJMIIV), to extract all possible feature relations with minimum computational cost. The performance of the proposed framework is compared with some of the state-of-the-art frameworks on various benchmark datasets. The comparison results using four well-known classifiers show that the proposed method can effectively improve the classification performance.