A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset

Feature selection is commonly employed for identifying the top n features that significantly contribute to desired prediction, example, find 50 or 100 genes responsible lung kidney cancer out of 50,000 genes. Thus, it a huge time- and resource-consuming practice. In this work, we propose divide-and-conquer technique with fuzzy backward feature elimination (FBFE) helps important quickly accurately. To show robustness proposed method, applied eight different datasets taken from NCBI database. We compare method seven state-of-the-art methods can obtain fast better classification accuracy. The will work qualitative, quantitative, continuous, discrete datasets. A web service developed researchers academicians select features.