基于ReliefF和蚁群算法的特征基因选择方法

针对高维小样本的DNA微阵列数据多分类问题，提出一种基于ReliefF和蚁群算法的特征基因选择方法(ReliefF and Ant Colony Optimization, ReFACO)。该方法首先采用ReliefF算法评估特征权重，根据阈值筛选出无关基因；然后引入改进的蚁群算法，在迭代改进的过程中寻找最优基因子集；最后利用经典分类算法对维数约简后的数据分类识别。经实验证明，该方法可以有效地剔除无关和冗余基因，并利用较少特征基因达到较高多分类效果。

Feature Gene Selection Method Based on ReliefF and Ant Colony Optimization

Aiming at the multi-classification problem of DNA microarray data with the characteristic of high dimension and small sample, this paper proposed a feature gene selection algorithm based on ReliefF and ant colony optimization (ReFACO). The method adopted ReliefF algorithm to evaluate the feature weights, and select the irrelevant genes based on the threshold, introduced an improved ant colony algorithm to find the optimal subset of genes in the process of iteration and improvement, used classical classification algorithms to classify and identify the data set, dimensions of which had been reduced. Experimental results show that the method can eliminate irrelevant and redundant genes effectively, and achieve a higher classification performance with less characteristic genes.