Logistic Regression Ensemble (LORENS) Applied to Drug Discovery

Authors

  • T Dwi Ary Widhianingsih Institut Teknologi Sepuluh Nopember https://orcid.org/0000-0003-2585-6895
  • Heri Kuswanto Institut Teknologi Sepuluh Nopember
  • Dedy Dwi Prastyo Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.11113/matematika.v36.n1.1197

Abstract

Logistic regression is one of the commonly used classification methods. It has some advantages, specifically related to hypothesis testing and its objective function. However, it also has some disadvantages in the case of high-dimensional data, such as multicolinearity, over-fitting, and a high computational burden. Ensemblebased classification methods have been proposed to overcome these problems. The logistic regression ensemble (LORENS) method is expected to improve the classification performance of basic logistic regression. In this paper, we apply it to the case of drug discovery with the objective of obtaining candidate compounds to protect the normal non-cancerous cells, which is considered to be a problem with a data-set of high dimensionality. The experimental results show that it performs well, with an accuracy of 69% and AUC of 0.7306.

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Published

2020-03-31

How to Cite

Widhianingsih, T. D. A., Kuswanto, H., & Prastyo, D. D. (2020). Logistic Regression Ensemble (LORENS) Applied to Drug Discovery. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 36(1), 43–49. https://doi.org/10.11113/matematika.v36.n1.1197

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Articles