提出一种基于虚拟可重构结构的内部演化硬件癌症分子分型方法. 为有效处理DNA微阵列数据和便于硬件实现，对比研究了5 种基于过滤模式的信息基因选择方法. 演化硬件通过系统学习和系统分类两个阶段对经过特征选择的信息基因进行处理. 对急性白血病数据集的实验结果表明，基于信噪比信息基因选择方法的演化硬件分类器识别率最高. 演化硬件具有和其他传统模式识别方法可比的识别率，识别时间仅需0.12 ms.

A virtual reconfigurable architecture-based intrinsic evolvable hardware (EHW) is proposed for the molecular classification of cancer. To efficiently process DNA microarray datasets and cooperate with the hardware realization of EHW, five different filter-based gene selection methods are compared and discussed in this paper. The EHW classification system handles the selected informative genes through two stages: system
learning and system classification. Empirical studies on a human acute leukemia dataset demonstrate that classification accuracy of the gene selection scheme based on signal-to-noise ratio outperforms its competitors. Classification accuracy of the proposed EHW is high comparable with other state-of-the-art pattern recognition methods. The system recognition time is reduced to 0.12 ms.