Bias and stability of single variable classifiers for feature ranking and selection
作者：
Highlights：
• We show that SVC feature ranking is highly sensitive to the choice of classifiers.
• Ranking and classification with the same classifier is not always the best approach.
• NB and AB generate better results than KNN and RF when used in both roles.
• Multiclassifier ranking ensembles perform above average but not at the overall best.
• We should also account for classifier parameter setting in SVC feature ranking.
摘要
•We show that SVC feature ranking is highly sensitive to the choice of classifiers.•Ranking and classification with the same classifier is not always the best approach.•NB and AB generate better results than KNN and RF when used in both roles.•Multiclassifier ranking ensembles perform above average but not at the overall best.•We should also account for classifier parameter setting in SVC feature ranking.
论文关键词：Feature ranking,Feature selection,Bias,Stability,Single variable classifier,Dimension reduction,Support Vector Machines,Naïve Bayes,Multilayer Perceptron,K-Nearest Neighbors,Logistic Regression,AdaBoost,Random Forests
论文评审过程：Available online 15 May 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.05.007
