Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems

Nadia Abd-Alsabour, Marcus Randall, Andrew Lewis

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

3 Citations (Scopus)

Abstract

The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) different stopping criteria when solving feature selection by ACO, and 4) experiments on an ACO algorithm for feature selection problems using artificial and real-world datasets in two cases fixing and not fixing the length of the selected feature subsets with the use of a support vector machine (SVM) classifier. The results showed that not fixing the length of the selected feature subsets is better than fixing the length of the selected feature subsets in terms of the classifier accuracy in seven datasets out of ten.

Original languageEnglish
Title of host publicationProceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012
Place of PublicationUnited States
PublisherWiley-IEEE Press
Pages733-738
Number of pages6
ISBN (Print)9780769548791
DOIs
Publication statusPublished - 2012
Event13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012 - Beijing, Beijing, China
Duration: 14 Dec 201216 Dec 2012

Conference

Conference13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012
CountryChina
CityBeijing
Period14/12/1216/12/12

Fingerprint

Feature Subset Selection
Ant colony optimization
Set theory
Optimization Algorithm
Subset
Feature extraction
Classifiers
Feature Selection
Classifier
Support vector machines
Subset Selection
Stopping Criterion
Support Vector Machine
Experiments
Experiment

Cite this

Abd-Alsabour, N., Randall, M., & Lewis, A. (2012). Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. In Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012 (pp. 733-738). [6589368] United States: Wiley-IEEE Press. https://doi.org/10.1109/PDCAT.2012.84
Abd-Alsabour, Nadia ; Randall, Marcus ; Lewis, Andrew. / Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012. United States : Wiley-IEEE Press, 2012. pp. 733-738
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Abd-Alsabour, N, Randall, M & Lewis, A 2012, Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. in Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012., 6589368, Wiley-IEEE Press, United States, pp. 733-738, 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012, Beijing, China, 14/12/12. https://doi.org/10.1109/PDCAT.2012.84

Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. / Abd-Alsabour, Nadia; Randall, Marcus; Lewis, Andrew.

Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012. United States : Wiley-IEEE Press, 2012. p. 733-738 6589368.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AB - The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) different stopping criteria when solving feature selection by ACO, and 4) experiments on an ACO algorithm for feature selection problems using artificial and real-world datasets in two cases fixing and not fixing the length of the selected feature subsets with the use of a support vector machine (SVM) classifier. The results showed that not fixing the length of the selected feature subsets is better than fixing the length of the selected feature subsets in terms of the classifier accuracy in seven datasets out of ten.

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Abd-Alsabour N, Randall M, Lewis A. Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. In Proceedings - 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012. United States: Wiley-IEEE Press. 2012. p. 733-738. 6589368 https://doi.org/10.1109/PDCAT.2012.84