A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures

Ensemble learning have emerged as a useful machine learning technique, which is based on the idea that combining the output of multiple models instead of using a single model. This practice, known as "diversity", and it usually enhances the performance. On other hand, ensemble feature selection method is based on the same idea, where multiple feature subsets are combined to select an optimal subset of features. Learning methods have difficulties with the dimensionality curse that impact the performance and increase the time exponentially. To overcome this issue, we propose a parallel heterogeneous ensemble feature selection based on three wellregarded algorithms: genetic algorithm, particle swarm optimizer, and grey wolf optimizer. The proposed approach is based on four phases; namely, distribution phase, parallel ensemble feature selection phase, combining and aggregation phase, and testing phase. Three implementations of the proposed approach are presented: a sequential approach running on the central processing unit (CPU), a parallel approach running on multi-core CPU, and a parallel approach running on multi-core CPU with graphics processing units (GPU). To assess the performance of the proposed approach twenty-one large datasets were used. The results show that the proposed parallel approach improved the performance in terms of the prediction results and running time.