Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

ABSTRACT: Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random su

SUBMITTER: Haque MN 

PROVIDER: S-EPMC4713117 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature