B 2 FSE framework for high dimensional imbalanced data: A case study for drug toxicity prediction

Abstract

Abstract The life of people is imperiled by umpteen chemicals unwittingly through the diverse sources like food, cleaning products, medicines, etc. At times, these chemicals can be toxic. Assessing and analyzing the toxicity of these chemical compounds can lead us to prospects to improve the environmental chemicals and invent new medicines. Tox21 crowdsourcing program initiative brings an evolutionary breakthrough for the researchers to develop better toxicity assessment techniques. Machine learning has received much attention in the domain of predictive analytics as it applies computational statistics and offers automation environment to expedite the data modeling process. The goal is to develop an efficient prediction model, combined with the machine learning algorithmic characteristics, which can predict whether a chemical compound is toxic and can affect the health adversely or not. Hence, an efficient pre-processing method should be adopted to achieve the best performance of the machine learning classifier. This work is a specific case study which proposes a Better Balanced Feature Selection Ensemble(B2FSE) framework for the classification of drug toxicity molecules, carried out on imbalanced and high dimensional complex drug data. We show that, an ensemble feature selection and an ensemble classifier, integrated with random subset selection, and a class balancer have the potential to generate more accurate, lower cost, and balanced classification framework. The performance of the proposed framework, when evaluated with different evaluation parameters and compared with the state-of-the art methods like SVM, random forest, bagging, etc., is found to be superior than the available methods.