期刊
INFORMATION FUSION
卷 45, 期 -, 页码 227-245出版社
ELSEVIER
DOI: 10.1016/j.inffus.2018.02.007
关键词
Ensemble learning; Feature selection; Automatic thresholding
资金
- Spanish Ministerio de Economa y Competitividad [TIN 2015-65069-C2-1-R]
- Xunta de Galicia [GRC2014/035]
- Xunta de Galicia (Centro Singular de Investigacion de Galicia)
- European Union (FEDER/ERDF)
Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble introduces the need for an aggregation step to combine all the output methods that confirm the ensemble. Besides, when trying to improve computational efficiency, ranking methods that order all initial features are preferred, and so an additional thresholding step is also mandatory. In this work two different ensemble designs based on ranking methods are described. The main difference between them is the order in which the combination and thresholding steps are performed. In addition, a new automatic threshold based on the combination of three data complexity measures is proposed and compared with traditional thresholding approaches based on retaining a fixed percentage of features. The behavior of these methods was tested, according to the SVM classification accuracy, with satisfactory results, for three different scenarios: synthetic datasets and two types of real datasets (where sample size is much higher than feature size, and where feature size is much higher than sample size).
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