一种改进的特征子集区分度评价准则

针对特征子集区分度准则(Discernibility of feature subsets, DFS)没有考虑特征测量量纲对特征子集区分能力影响的缺陷, 引入离散系数, 提出GDFS (Generalized discernibility of feature subsets)特征子集区分度准则. 结合顺序前向、顺序后向、顺序前向浮动和顺序后向浮动4种搜索策略, 以极限学习机为分类器, 得到4种混合特征选择算法. UCI数据集与基因数据集的实验测试, 以及与DFS、Relief、DRJMIM、mRMR、LLE Score、AVC、SVM-RFE、VMInaive、AMID、AMID-DWSFS、CFR和FSSC-SD的实验比较和统计重要度检测表明: 提出的GDFS优于DFS, 能选择到分类能力更好的特征子集.

An Improved Criterion for Evaluating the Discernibility of a Feature Subset

To overcome the deficiencies of the discernibility of feature subsets (DFS) which cannot take into account the influences from different attribute scales on the discernibility of a feature subset, the generalized DFS, shorted as GDFS, is proposed in this paper by introducing the coefficient of variation. The GDFS is combined with four search strategies, including sequential forward search (SFS), sequential backward search (SBS), sequential forward floating search (SFFS) and sequential backward floating search (SBFS) to develop four hybrid feature selection algorithms. The extreme learning machine (ELM) is adopted as a classification tool to guide feature selection process. We test the classification capability of the feature subsets detected by GDFS on the datasets from UCI machine learning repository and on the classic gene expression datasets, and compare the performance of the ELM classifiers based on the feature subsets by GDFS, DFS and classic feature selection algorithms including Relief, DRJMIM, mRMR, LLE Score, AVC, SVM-RFE, VMInaive, AMID, AMID-DWSFS, CFR, and FSSC-SD respectively. The statistical significance test is also conducted between GDFS, DFS, Relief, DRJMIM, mRMR, LLE Score, AVC, SVM-RFE, VMInaive, AMID, AMID-DWSFS, CFR, and FSSC-SD. Experimental results demonstrate that the proposed GDFS is superior to the original DFS. It can detect the feature subsets with much better capability in classification performance.