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Improving Floating Search Feature Selection using Genetic Algorithm

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 3 (2017) · pp. 299–317

DOI
10.5614/itbj.ict.res.appl.2017.11.3.6
ISSN
2338-5499, 2337-5787
Published
2017-12-22
Publisher
DRI - ITB
Language
eng

Abstract

Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a critical step in classification, especially when applied to large datasets. In this article, a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes is proposed. A genetic algorithm is employed to improve the quality of the features selected by the floating search method in each iteration. A criterion function is applied to select relevant and high-quality features that can improve classification accuracy. The proposed method was evaluated using 20 standard machine learning datasets of various size and complexity. The results show that the proposed method is effective in general across different classifiers and performs well in comparison with recently reported techniques. In addition, the application of the proposed method with support vector machine provides the best performance among the classifiers studied and outperformed previous researches with the majority of data sets.

Keywords

Collaboration network of the authors

6 authors · 5 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • National Institute of Development Administration
  • Kasetsart University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Ajay ArunachalamAffiliation unknown2
Theerapol LimsattaNational Institute of Development Administration1
Jirawan CharoensukKasetsart University1
Knitchepon ChotchantarakunNational Institute of Development Administration1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Improving Floating Search Feature Selection using Genetic Algorithm</dc:title>
	<dc:creator>Homsapaya, Kanyanut</dc:creator>
	<dc:creator>Sornil, Ohm</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">evaluation</dc:subject>
	<dc:subject xml:lang="en-US">feature selection</dc:subject>
	<dc:subject xml:lang="en-US">floating search</dc:subject>
	<dc:subject xml:lang="en-US">genetic algorithm.</dc:subject>
	<dc:description xml:lang="en-US">Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a critical step in classification, especially when applied to large datasets. In this article, a novel filter-based floating search technique for feature selection to select an optimal set of features for classification purposes is proposed. A genetic algorithm is employed to improve the quality of the features selected by the floating search method in each iteration. A criterion function is applied to select relevant and high-quality features that can improve classification accuracy. The proposed method was evaluated using 20 standard machine learning datasets of various size and complexity. The results show that the proposed method is effective in general across different classifiers and performs well in comparison with recently reported techniques. In addition, the application of the proposed method with support vector machine provides the best performance among the classifiers studied and outperformed previous researches with the majority of data sets.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-12-22</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/3375</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.3.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 3 (2017); 299-317</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/3375/3049</dc:relation>
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