Improvement the performance of the classification models of Cyclooxygenase-2 inhibitors using undersampling methods based on the rivality and reliability indexes

Abstract

Undersampling, prototype or instance selection techniques are oriented to remove redundant and noisy molecules from the datasets in order to reduce the computational cost and the necessity of memory in the construction of QSAR models, maintaining the performance of the models. In this paper, we describe and apply an undersampling technique based on the rivality and reliability indexes to the building of classification models for two Cyclooxinase-2 inhibitors datasets. In a preprocessing stage, the datasets are analyzed and curated and classification models are built using Support Vector Machine, Random Forest and Rivality Index Neighborhood algorithms. The results obtained clearly improve the ones described in the literature for these datasets for the training models and external validations carried out. Values of Matthews Correlation Coefficient higher than 0.9 for the training models and external validations proved the high robustness of the models generated using the undersampling technique capable of generating reductions of the datasets greater than 80%.