Big data classification method of neighborhood search for online feature selection

In order to solve the problem of low efficiency of existing algorithms when dealing with massive data sets, a parallel big data classification method is proposed based on neighborhood search for online feature selection. In the Map phase, the big data set is divided into splits. And the dynamic unknown feature space is optimized by the firefly algorithm and simulated annealing algorithm, the neighborhood search is carried out for the online features, then selecting the best feature. The obtained feature set is used as the input feature of the Reduce stage, and then the kernel support vector machine is used to classify the data. Experimental results show that the proposed method is superior to other existing methods in terms of accuracy, recall, F value and time.