uEFS: An efficient and comprehensive ensemble-based feature selection methodology to select informative features

PloS One
Maqbool AliMaqbool Hussain

Abstract

Feature selection is considered to be one of the most critical methods for choosing appropriate features from a larger set of items. This task requires two basic steps: ranking and filtering. Of these, the former necessitates the ranking of all features, while the latter involves filtering out all irrelevant features based on some threshold value. In this regard, several feature selection methods with well-documented capabilities and limitations have already been proposed. Similarly, feature ranking is also nontrivial, as it requires the designation of an optimal cutoff value so as to properly select important features from a list of candidate features. However, the availability of a comprehensive feature ranking and a filtering approach, which alleviates the existing limitations and provides an efficient mechanism for achieving optimal results, is a major problem. Keeping in view these facts, we present an efficient and comprehensive univariate ensemble-based feature selection (uEFS) methodology to select informative features from an input dataset. For the uEFS methodology, we first propose a unified features scoring (UFS) algorithm to generate a final ranked list of features following a comprehensive evaluation of a feature s...Continue Reading

References

Apr 27, 2005·Journal of Bioinformatics and Computational Biology·Chris Ding, Hanchuan Peng
Mar 2, 2006·Environmental Technology·Y J HuangHung-Ta Chen
Dec 16, 2006·ACS Chemical Biology·Monya Baker
Aug 28, 2007·Bioinformatics·Yvan SaeysPedro Larrañaga
May 20, 2014·Applied Artificial Intelligence : AAI·Chandrima SarkarJaideep Srivastava

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Software Mentioned

uEFS
OneR
EMFFS
WEKA
DDKAT
GitHub
GR
SplitInfo

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