Multi-Criteria Decision Making: The Best Choice for the Modeling of Chemicals Against Hyper-Pigmentation?

Classifier ensembles appeared to be powerful alternative for handling a difficult problem. It is rapidly growing and enjoying many attentions from pattern recognition and machine learning communities. In the present report, the potential of multi-criteria decision making via multiclassifier approaches is assessed by applying them in the modeling of chemicals against hyper-pigmentation. TOMOCOMD-CARDD atom-based quadratic indices are used as descriptors to parameterize the molecular structures. Support vector machine, artificial neural network, Bayesian network, binary logistic regression, instance-based learning and tree classification applied on two collected datasets are explored as standalone classifiers. Prediction sets (PSs) are used to assess the performance of multiclassifier systems (MCSs). A strategy exploiting the principal component analysis together with pairwise diversity measures is designed to select the most diverse base classifiers to combine. Various trainable and nontrainable systems are developed that aggregate, at the abstract and continuous levels, the outputs of base classifiers. The obtained results are rather encouraging since the MCSs generally enhance the performance of the base classifiers; e.g. the best MCS obtains global accuracy of 95.51%, 88.89% in the PS for the data I and II in regard to 94.12% and 85.93% of best individual classifier, respectively. Our results suggest that the MCSs could be the best choice till the moment to obtain suitable QSAR models for the prediction of depigmenting agents. Finally, we consider these approaches will aid improving the virtual screening procedures and increasing the practicality of data mining of chemical datasets for the discovery of novel lead compounds.