An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data

It is common that imbalanced datasets are often generated from high-throughput screening (HTS). For a given dataset without taking into account the imbalanced nature, most classification methods tend to produce high predictive accuracy for the majority class, but significantly poor performance for the minority class. In this work, an efficient algorithm, GLMBoost, coupled with Synthetic Minority Over-sampling TEchnique (SMOTE) is developed and utilized to overcome the problem for several imbalanced datasets from PubChem BioAssay. By applying the proposed combinatorial method, those data of rare samples (active compounds), for which usually poor results are generated, can be detected apparently with high balanced accuracy (Gmean). As a comparison with GLMBoost, Random Forest (RF) combined with SMOTE is also adopted to classify the same datasets. Our results show that the former (GLMBoost + SMOTE) not only exhibits higher performance as measured by the percentage of correct classification for the rare samples (Sensitivity) and Gmean, but also demonstrates greater computational efficiency than the latter (RF + SMOTE). Therefore, we hope that the proposed combinatorial algorithm based on GLMBoost and SMOTE could be extensively used to tackle the imbalanced classification problem.