OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.

ABSTRACT: Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several r

SUBMITTER: Magana-Mora A 

PROVIDER: S-EPMC5478657 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature