An algorithm acceleration framework for correlation-based feature selection

Repeated calculations lead to a sharp increase in the time of correlation-based feature selection. Incremental iteration has been applied in some algorithms to improve the efficiency. However, the computational efficiency of correlation has generally be ignored. An algorithm acceleration framework for correlation-based feature selection (AFCFS) is proposed. In AFCFS, the criterion of the feature selection will be analyzed and reconstructed based on entropy granularity, and the algorithm structure will also be adjusted accordingly. Specifically, all repeated part of calculation will be saved in mapping tables and can be accessed in next time directly, so as to further reduce the calculation repetition rate and improve the efficiency. The experimental results show that AFCFS can greatly reduce the cost time of these algorithms, and keep the corresponding classification accuracy basically unchanged.