A comparative review of regression ensembles on drug design datasets

Authors : Mehmet Fatih AMASYALI, Okan ERSOY

Pages : 586-602

Doi:10.3906/elk-1102-1033

View : 12 | Download : 12

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :Drug design datasets are usually known as hard-modeled, having a large number of features and a small number of samples. Regression types of problems are common in the drug design area. Committee machines insert ignore into journalissuearticles values(ensembles); have become popular in machine learning because of their good performance. In this study, the dynamics of ensembles used in regression-related drug design problems are investigated with a drug design dataset collection. The study tries to determine the most successful ensemble algorithm, the base algorithm--ensemble pair having the best/worst results, the best successful single algorithm, and the similarities of algorithms according to their performances. We also discuss whether ensembles always generate better results than single algorithms.
Keywords : Drug design datasets, ensemble algorithms, regression, regression ensembles