Enhancing Binary Classification Performance in Biomedical Datasets: Regularized ELM with SMOTE and Quantile Transforms Focused on Breast Cancer Analysis

Using microarray datasets, this research investigation addresses the problem of unbalanced data in binary classification tasks. The objective is to increase classification performance by adding Extreme Learning Machine (ELM) regularization, as well as Synthetic Minority Over-sampling Technique (SMOT...

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Bibliographic Details
Main Authors:	Brilliant Friezka Aina, Meta Kallista, Ig. Prasetya Dwi Wibawa, Ginaldi Ari Nugroho, Ivana Meiska, Syifa Melinda Naf’an
Format:	Article
Language:	English
Published:	Mathematics Department UIN Maulana Malik Ibrahim Malang 2024-11-01
Series:	Cauchy: Jurnal Matematika Murni dan Aplikasi
Subjects:	regularized extreme learning machine (r-elm) biomedical synthetic minority over-sampling technique (smote) quantile transformer.
Online Access:	https://ejournal.uin-malang.ac.id/index.php/Math/article/view/28785
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Enhancing Binary Classification Performance in Biomedical Datasets: Regularized ELM with SMOTE and Quantile Transforms Focused on Breast Cancer Analysis

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