A novel hybrid gene selection for tumor identification by combining multifilter integration and a recursive flower pollination search algorithm

Gene selection is crucial to tumor identification based on microarray expression data. The identification of genes with strong discriminative power has been a hot research topic and a great challenge in research related to machine learning-based tumor identification. This study proposes a novel hybrid gene selection strategy for tumor identification by combining multifilter integration and a recursive flower pollination algorithm (MFI-RFPA) to select a minimum number of genes to achieve optimal classification performance. MFI-RFPA selects genes that involve two stages. In the first stage, most irrelevant and noise genes are eliminated by using a combination of three filters based on different metrics. In this process, multiple filters based on different metrics are combined in the voting, which makes the filter process of MFI-RFPA more robust. In the second stage, MFI-RFPA searches for the optimal subset of the remaining genes by using a recursive flower pollination algorithm (RFPA). In this process, the flower pollination algorithm is performed recursively without reducing the classification accuracy, which ensures that a small and compact subset of genes with excellent classification performance is searched. In the experimental part, MFI-RFPA is compared with both its directly related methods (such as MFI, FPA, RFPA, MFI-FPA) and other state-of-the-art methods. The excellent performance of MFI-RFPA in reducing the number of genes and improving the classification accuracy can be reflected in the comparison of experiments.