Sorting-Based Dynamic Classifier Ensemble Selection

Abstract

In ensemble learning, a higher accuracy can be achieved by integrating some classifiers instead of all the classifiers. But, it is very difficult to select the best classifier combination which can be seen as an optimization problem, from a pool of classifiers. To deal with this problem, we propose a new classifier selection method, Sorting-based Dynamic Classifier Ensemble Selection (SDES), which consists of two stages: (1) classifier sorting, and (2) dynamic ensemble selection on sorted classifier sequence. In the first stage, classifiers are sorted based on diversity, to avoid searching for the nearest neighbors in dynamic ensemble selection methods and greatly improve the selection efficiency. In the second stage, the optimal subset of classifiers is selected from the sorted classifier sequence based on confidence of test samples, to guarantee high accuracy of the optimal classifier subset. Experimental results have shown the effectiveness and high efficiency of the proposed method.