TY - JOUR AU - Widhianingsih, T Dwi Ary AU - Kuswanto, Heri AU - Prastyo, Dedy Dwi PY - 2020/03/31 Y2 - 2024/03/28 TI - Logistic Regression Ensemble (LORENS) Applied to Drug Discovery JF - MATEMATIKA JA - MATEMATIKA VL - 36 IS - 1 SE - Articles DO - 10.11113/matematika.v36.n1.1197 UR - https://matematika.utm.my/index.php/matematika/article/view/1197 SP - 43-49 AB - 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. ER -