Variable selection methods for multi-class classification using signomial function
提出几种使用符号函数的变量选择方法，通过考虑所有类别来选取相关变量，并自动确定变量数量，同时直接得到分类器，分类精度与现有方法相当或更优。
We develop several variable selection methods using signomial function to select relevant variables for multi-class classification by taking all classes into consideration. We introduce a -norm regularization function to measure the number of selected variables and two adaptive parameters to apply different importance weights for different variables according to their relative importance. The proposed methods select variables suitable for predicting the output and automatically determine the number of variables to be selected. Then, with the selected variables, they naturally obtain the resulting classifiers without an additional classification process. The classifiers obtained by the proposed methods yield competitive or better classification accuracy levels than those by the existing methods.