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

Citation

Ali, M and Ali, SI and Kim, D and Hur, T and Bang, J and Lee, S and Kang, BH and Hussain, M, uEFS: An efficient and comprehensive ensemble-based feature selection methodology to select informative features, PLOS One, 13, (8) Article e0202705. ISSN 1932-6203 (2018) [Refereed Article]


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Copyright Statement

Copyright 2018 Ali et al. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0202705

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 set. For defining cutoff points to remove irrelevant features, we subsequently present a threshold value selection (TVS) algorithm to select a subset of features that are deemed important for the classifier construction. The uEFS methodology is evaluated using standard benchmark datasets. The extensive experimental results show that our proposed uEFS methodology provides competitive accuracy and achieved (1) on average around a 7% increase in f-measure, and (2) on average around a 5% increase in predictive accuracy as compared with state-of-the-art methods.

Item Details

Item Type:Refereed Article
Keywords:univariate ensemble-based feature selection (uEFS) methodology
Research Division:Information and Computing Sciences
Research Group:Data Format
Research Field:Data Structures
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
UTAS Author:Ali, M ( Maqbool Ali)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:128254
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2018-09-11
Last Modified:2019-02-26
Downloads:76 View Download Statistics

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