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

Authors

  • Supri Bin Hj Amir Universitas Hasanuddin
  • Bagas Prasetyo

DOI:

https://doi.org/10.20956/jmsk.v17i1.10899

Keywords:

Data mining, Imbalance Datasets, Classification, Elliptic Envelope, Isolation Forest

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.

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References

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Published

2020-08-24

How to Cite

Amir, S. B. H., & Prasetyo, B. (2020). Comparison of Elliptic Envelope Method and Isolation Forest Method on Imbalance Dataset. Jurnal Matematika, Statistika Dan Komputasi, 17(1), 42-49. https://doi.org/10.20956/jmsk.v17i1.10899

Issue

Vol. 17 No. 1 (2020): JMSK, SEPTEMBER, 2020

Section

Research Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Jurnal Matematika, Statistika dan Komputasi is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution License, allowing third parties to copy and redistribute the material in any medium or format, transform, and build upon the material, provided the original work is properly cited and states its license.  This license allows authors and readers to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference.

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