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Comparison of Elliptic Envelope Method and Isolation Forest Method on Imbalance Dataset

Jurnal Matematika, Statistika dan Komputasi · Vol. 17 No. 1 (2020) · pp. 42–49

DOI
10.20956/jmsk.v17i1.10899
ISSN
2614-8811, 1858-1382
Published
2020-08-24
Publisher
Department of Mathematics, Hasanuddin University
Language
en

Abstract

The problem of unbalanced data is important in the field of Data Mining. Dataset with unbalanced classes is a dataset whose frequency of occurrence of certain classes is very much different from other classes. This imbalance problem will bias the classifier's performance. Many researchers have examined both the development of algorithms and modifications to the preprocessing stage to overcome this problem. This study discusses the comparison of One Class Classification algorithms, namely Elliptic Envelope and Isolation Forest on unbalanced data. From this study, the Elliptic Envelope Method showed better results compared to the Isolation Forest method with 80.28% recall testing and 80.28% precision while Isolation Forest showed 46.95% recall results and 46.95% precision.

Keywords

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Nur Hilal A. SyahrirAffiliation unknown1
Sumarheni SumarheniAffiliation unknown1
Hedi KuswantoAffiliation unknown1

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://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of Elliptic Envelope Method and Isolation Forest Method on Imbalance Dataset</dc:title>
	<dc:creator xml:lang="en">Amir, Supri Bin Hj</dc:creator>
	<dc:creator xml:lang="en">Prasetyo, Bagas</dc:creator>
	<dc:subject xml:lang="en">Data mining</dc:subject>
	<dc:subject xml:lang="en">Imbalance Datasets</dc:subject>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Elliptic Envelope</dc:subject>
	<dc:subject xml:lang="en">Isolation Forest</dc:subject>
	<dc:description xml:lang="en">The problem of unbalanced data is important in the field of Data Mining. Dataset with unbalanced classes is a dataset whose frequency of occurrence of certain classes is very much different from other classes. This imbalance problem will bias the classifier's performance. Many researchers have examined both the development of algorithms and modifications to the preprocessing stage to overcome this problem. This study discusses the comparison of One Class Classification algorithms, namely Elliptic Envelope and Isolation Forest on unbalanced data. From this study, the Elliptic Envelope Method showed better results compared to the Isolation Forest method with 80.28% recall testing and 80.28% precision while Isolation Forest showed 46.95% recall results and 46.95% precision.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2020-08-24</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://journal.unhas.ac.id/index.php/jmsk/article/view/10899</dc:identifier>
	<dc:identifier>10.20956/jmsk.v17i1.10899</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 17 No. 1 (2020): JMSK, SEPTEMBER, 2020; 42-49</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/10899/pdf</dc:relation>
	<dc:relation>10.20956/j.v17i1.10899.g5720</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Author and publisher</dc:rights>
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