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Nowadays, in real-world applications, the dimensions of data are generated dynamically, and the traditional batch feature selection methods are not suitable for streaming data. So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher number of features selected. In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum relevant features, and also overcomes the drawbacks of existing online streaming feature selection methods. To increase the performance speed of the proposed method parallel processing is used. To evaluate the performance of the proposed online feature selection method k-NN, SVM, and Decision Tree Classifiers are used and compared against the state-of-the-art online feature selection methods. Evaluation metrics like Accuracy, Precision, Recall, F1-Score are used on benchmark datasets for performance analysis. From the experimental analysis, it is proved that the proposed method has achieved more than 95% accuracy for most of the datasets and performs well over other existing online streaming feature selection methods and also, overcomes the drawbacks of the existing methods.

Fuzzy Rank Based Parallel Online Feature Selection Method using Multiple Sliding Windows