Improving Classifier Fusion Using Particle Swarm Optimization

Kalyan Veeramachaneni, Weizhong Yan, Kai Goebel, Lisa Osadciw
2007 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making  
Both experimental and theoretical studies have proved that classifier fusion can be effective in improving overall classification performance. Classifier fusion can be performed on either score (raw classifier outputs) level or decision level. While tremendous research interests have been on score-level fusion, research work for decision-level fusion is sparse. This paper presents a particle swarm optimization based decision-level fusion scheme for optimizing classifier fusion performance.
more » ... ple classifiers are fused at the decision level, and the particle swarm optimization algorithm finds optimal decision threshold for each classifier and the optimal fusion rule. Specifically, we present an optimal fusion strategy for fusing multiple classifiers to satisfy accuracy performance requirements, as applied to a real-world classification problem. The optimal decision fusion technique is found to perform significantly better than the conventional classifier fusion methods, i.e., traditional decision level fusion and averaged sum rule.
doi:10.1109/mcdm.2007.369427 dblp:conf/cimcdm/VeeramachaneniYGO07 fatcat:j7emsd4ahbgpjawpd4klhymami