Recently, many studies based on microRNAs (miRNAs) showed a new aspect of cancer classification, and feature selection methods are used to reduce the high dimensionality of miRNA expression data. These methods just consider the problem of where feature to class is 1:1 or n:1. But one miRNA may have influence to more than one type of cancers. However, these miRNAs are considered to be low ranked in traditional feature selection methods and they are removed at most of time. Therefore, it is

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... ary to consider the problem of 1:n or m:n during feature selection. In our wok, we considered both high and low-ranking features to cover all problems (1:1, n:1, 1:n, m:n) in cancer classification. After numerous tests, information gain and chi-squared feature selection methods were chosen to select the high and low-ranking features to form the m-to-n feature subset, and LibSVM classifier was used to do the multi-class classification. Our results demonstrate that the m-to-n features make a positive impression of low-ranking microRNAs in cancer classification since they lead to achieve higher classification accuracy compared with the traditional feature selection methods.