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

ABSTRACT: 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 w

SUBMITTER: Hao M 

PROVIDER: S-EPMC3884825 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature