Logistic Regression Ensemble (LORENS) Applied to Drug Discovery
| Autor: | Heri Kuswanto, Dedy Dwi Prastyo, T. Dwi Ary Widhianingsih |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
010102 general mathematics 0102 computer and information sciences Logistic regression Machine learning computer.software_genre 01 natural sciences 010201 computation theory & mathematics Multicollinearity Classification methods Artificial intelligence 0101 mathematics High dimensionality business computer Statistical hypothesis testing Mathematics |
| Zdroj: | MATEMATIKA. 36:43-49 |
| ISSN: | 0127-9602 0127-8274 |
| DOI: | 10.11113/matematika.v36.n1.1197 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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