P. Bo and F. Jufu (PRC)
Feature Selection; Gene selection; Regression-Test model; Pattern Recognition; Bio-informatics;
Feature selection is a classical problem in pattern recognition. Feature selection when the number of features is much higher than the number of samples is a new issue that we seldom face before. But now more and more such problems emerge out. Gene selection, an important issue in medicine and biology [1], is such a problem. Many of traditional feature selection methods cannot perform well in that case. This paper proposes a Regression-Test model for high dimension feature selection. This method has a good performance when the dimension of feature space is very high. Experiment result in public data has demonstrated the validity of it.
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