An online streaming feature selection method based on the Choquet fuzzy integral

Feature selection is a data preprocessing technique used for high-dimensional data sets before machine learning and data mining algorithms. The feature selection aims to find a minimal and optimal subset of the feature set. This subset includes valuable features while not including redundant ones. To do this, many current feature selection methods require the entire feature at first, and if a new feature is added to the feature set in the future, the algorithm must be run from the beginning. However, it is impossible to get all the features in many real-world applications or even wait for them. Therefore, online feature selection methods are provided for such issues that the entire feature space is not available at first. This paper presents an online feature selection method using the concept of Choquet fuzzy integral. This method first evaluates feature flows based on several filter criteria. Then, based on the Choquet operator, their results are combined, and decisions are made to preserve or ignore the feature. In the evaluation step, the performance of the proposed algorithm is compared with six online feature selection methods based on two categories. The proposed method is based on the results obtained in five real-world datasets that achieve about two percent improvement over similar methods based on classification accuracy and F-Score criteria. Also, due to the simple calculations in the process of the proposed method, the evaluation of features is done in a short time.