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Feature Selection Method based on Grid Partition Data Field

Many traditional feature selection algorithms have been used to select the important features of nonpathogenic genes and pathogenic genes. It is great significance to reduce the number of the features. To face the problem of genetic data classification, a new feature selection method based on general data field (FR-GDF)has been proposed. But in FR-GDF, the choice of potential function is mainly Gaussian function, and the selection of anisotropic influence factor is traditionally density estimation. The quality value is set to 0 or 1 artificially. The formation speed of data field is slow, and there is a problem of repeated calculation. To solve the limitation of the FR-GDF, we propose a new feature selection model Feature Selection Method based on Grid Partition Data Field (FS-GPDF). Firstly, we make full use of the characteristics reflected by the data set itself, consider the spatial distribution of the samples. And then establish the nonlinear model and select the feature subset in the multi classification data. Finally, the model was used to select features. The experiments show that the method has breakthroughs in the discriminability and classification accuracy of the feature subset.