Enhancing Decision Combination in Classifier Committee via Positional Voting

In this work, we propose an approach for aggregating classifiers using positional voting techniques. We extend the positional voting by optimizing weights of the preferences to better aggregate the committee classifiers. Staring from initial weights determined by a voting algorithm the aggregating weights are optimized by a differential evolution algorithm. The algorithm has been evaluated on a human action dataset. We demonstrate experimentally that on SYSU 3DHOI dataset the proposed algorithm achieves superior results against recent algorithms including skeleton-based ones.