陈 涛.基于改进核模糊粗糙集的特征基因混合选择方法[J].中南民族大学学报自然科学版,2018,(2):121-127
基于改进核模糊粗糙集的特征基因混合选择方法
A Hybrid Feature Gene Selection Algorithm Based on an Improved Kernelized Fuzzy Rough Sets
  
DOI:
中文关键词: 基因表达谱  特征基因选择  ReliefF 方法  核模糊粗糙集  差分进化算法
英文关键词: gene expression profile  feature gene selection  ReliefF algorithm  kernelized fuzzy rough sets  differential evolution
基金项目:国家自然科学基金资助项目(11502132) ; 陕西省教育厅科研资助项目(16JK1149) ; 陕西理工大学科研资助项目( SLGQD2017-07)
作者单位
陈 涛 陕西理工大学 数学与计算机科学学院汉中 723000 
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中文摘要:
      针对基因表达谱高维、 小样本、 高冗余和高噪声等特点,提出了一种特征基因混合选择方法.采用 ReliefF 方法对原始基因进行排序,过滤无效基因,获得初选基因子集, 给出了基于差分进化算法优化的核模糊粗糙集模型,进行了特征基因终选.仿真实验结果表明:所提算法比 ReliefF、 KruskalWallis、 Gini Index 等算法在分类精度和基 因数量等方面有明显优势.
英文摘要:
      To address the problem of low accuracy and low effectiveness of current methods which can't adapt to learners' A hybrid feature gene selection algorithm based on an improved kernelized fuzzy rough sets aiming at the characteristics of high dimensions, small samples, high noise and high redundancy of gene expression profiles is proposed.The top-ranked genes based on ReliefF algorithm are selected to construct a primary gene subset in order to remove the invalid genes. An improved kernelized fuzzy rough sets model based on the differential evolution algorithm is proposed to achieve the selection of feature genes. Simulation results show that the proposed algorithm has obvious advantages comparison with ReliefF, KruskalWallis and Gini index algorithm.
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