The selection of feature genes with high recognition ability from the gene expression profiles have gained great significances in biology. However, most of the existing methods for feature genes selection have a high time complexity where lead to a poor performance. Motivated by this, an effective feature selection method, called Fisher transformation (FT), is proposed which based on the improved Fisher discriminant analysis (FDA) and neighborhood rough set algorithms. The FT method has two benefits: 1. The multiple neighborhood rough set algorithm is used for solving the small sample size problem of FDA; 2. The improved FDA algorithm is used for selecting feature genes and ameliorating poor ability of classification. Furthermore, we measure the impact of the FT approach on the final selection consequence. The results obtained on four public tumor microarray datasets provide beneficial insight on both the benefits and limitations, paving the way to the exploration of new and wider feature selection programs.
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