Many feature subset selection (FSS) algorithms have been proposed, but not
all of them are appropriate for a given feature selection problem. At the same
time, so far there is rarely a good way to choose appropriate FSS algorithms
for the problem at hand. Thus, FSS algorithm automatic recommendation is very
important and practically useful. In this paper, a meta learning based FSS
algorithm automatic recommendation method is presented. The proposed method
first identifies the data sets that are most similar to the one at hand by the
k-nearest neighbor classification algorithm, and the distances among these data
sets are calculated based on the commonly-used data set characteristics. Then,
it ranks all the candidate FSS algorithms according to their performance on
these similar data sets, and chooses the algorithms with best performance as
the appropriate ones. The performance of the candidate FSS algorithms is
evaluated by a multi-criteria metric that takes into account not only the
classification accuracy over the selected features, but also the runtime of
feature selection and the number of selected features. The proposed
recommendation method is extensively tested on 115 real world data sets with 22
well-known and frequently-used different FSS algorithms for five representative
classifiers. The results show the effectiveness of our proposed FSS algorithm
recommendation method.

本文提出了一种基于元学习的特征子集选择算法自动推荐方法，通过多个性能指标综合评估候选 FSS 算法的性能，利用 k 近邻分类算法识别相似数据集，从中筛选出表现最佳的 FSS 算法，出色地解决了选择合适 FSS 算法的问题。