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Jurnal SINTA 2

SIMILARITY BASED ENTROPY ON FEATURE SELECTION FOR HIGH DIMENSIONAL DATA CLASSIFICATION

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 2 (2014) · pp. 101–107

DOI
10.21609/jiki.v7i2.263
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Abstract Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we have computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features. The experiment result shows that proposed method is able to deal with high dimensional data problem with average accuracy of 80.5%.

Keywords

Collaboration network of the authors

40 authors · 94 co-authorship links · 12 across different organisations · showing the 40 most connected

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

  • Institut Teknologi Sepuluh Nopember
  • Lampung University
  • University of Brawijaya
  • Universitas Sembilanbelas November Kolaka
  • Universitas Islam Negeri Raden Intan Lampung
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Diana PurwitasariInstitut Teknologi Sepuluh Nopember6
Dini Adni NavastaraInstitut Teknologi Sepuluh Nopember4
Rarasmaya IndraswariInstitut Teknologi Sepuluh Nopember3
Munjiah Nur SaadahInstitut Teknologi Sepuluh Nopember2
Rigga Widar AtmagiInstitut Teknologi Sepuluh Nopember2
Dyah S. RahayuInstitut Teknologi Sepuluh Nopember2
Khoirul UmamInstitut Teknologi Sepuluh Nopember2
Gulpi Qorik Oktagalu PratamasunuAffiliation unknown2
Anny YuniartiInstitut Teknologi Sepuluh Nopember2
Darlis HerumurtiInstitut Teknologi Sepuluh Nopember2
Safri AdamInstitut Teknologi Sepuluh Nopember2
Chastine FatichahInstitut Teknologi Sepuluh Nopember2
Nanik SuciatiInstitut Teknologi Sepuluh Nopember2
Nurfitria NingsiUniversitas Sembilanbelas November Kolaka1
SarimuddinAffiliation unknown1
Wawan GunawanUniversitas Islam Negeri Raden Intan Lampung1
⁠Undang RosidinLampung University1
Nina KadaritnaLampung University1
Naser JawasInstitut Teknologi Sepuluh Nopember1
Randy Cahya WihandikaUniversity of Brawijaya1
Go Frendi GunawanInstitut Teknologi Sepuluh Nopember1
Sonny Christiano GosariaInstitut Teknologi Sepuluh Nopember1
Yuita Arum SariUniversity of Brawijaya1
Evy Kamilah RatnasariInstitut Teknologi Sepuluh Nopember1
Siti MutrofinInstitut Teknologi Sepuluh Nopember1
Fidi Wincoko PutroInstitut Teknologi Sepuluh Nopember1
Khadijah Fahmi Hayati HolleInstitut Teknologi Sepuluh Nopember1
Andi Baso KaswarInstitut Teknologi Sepuluh Nopember1
Saprina MamaseInstitut Teknologi Sepuluh Nopember1
Saiful Bahri MusaInstitut Teknologi Sepuluh Nopember1
Ahmad Mustofa HadiInstitut Teknologi Sepuluh Nopember1
Christian Sri Kusuma AdityaInstitut Teknologi Sepuluh Nopember1
Mamluatul Hani`ahInstitut Teknologi Sepuluh Nopember1
Aryo HartoInstitut Teknologi Sepuluh Nopember1
Arif FadllullahInstitut Teknologi Sepuluh Nopember1
Dasrit Debora KamudiInstitut Teknologi Sepuluh Nopember1

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://jiki.cs.ui.ac.id/index.php/jiki/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">SIMILARITY BASED ENTROPY ON FEATURE SELECTION FOR HIGH DIMENSIONAL DATA CLASSIFICATION</dc:title>
	<dc:creator>Sari, Jayanti Yusmah</dc:creator>
	<dc:creator>Muchtar, Mutmainnah</dc:creator>
	<dc:creator>Zarkasi, Mohammad</dc:creator>
	<dc:creator>Arifin, Agus Zainal</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">entropy</dc:subject>
	<dc:subject xml:lang="en-US">feature selection</dc:subject>
	<dc:subject xml:lang="en-US">high dimensional data</dc:subject>
	<dc:subject xml:lang="en-US">similarity.</dc:subject>
	<dc:description xml:lang="en-US">Abstract  Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we have computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features.  The experiment result shows that proposed method is able to deal with high dimensional data problem with average accuracy of 80.5%.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2014-08-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/263</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i2.263</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 7 No. 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 101-107</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v7i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/263/321</dc:relation>
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