高维基因表达数据集分类规则组的确定

目前基因表达数据分析的大多数算法都是探索性的选择基因来生成识别类（例如癌细胞组织与正常细胞组织）的规则组，对此本文提出了一种对基因表达数据集进行分类的稳健算法，确保可以找到最优的k个基因生成分类的规则组，并且通过试验表明由此算法得出的规则组比其他分类方法更准确．

Finding rule groups to classify high dimensional gene expression datasets

The most classification algorithm select dimensions （genes） heuristically to form rules groups to identify classes such as cancerous and normal tissues. This paper proposes a robust algorithm to find out rule groups to classify gene expres- sion datasets. Our algorithm can guarantee finding out best - k genes to form rule groups for the classification of expression datasets, and the experiments show that the rule groups obtained by our algorithm have higher accuracy than other classification approaches.