Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  presents a particle swarm optimization based decision-level fusion scheme for optimizing classifier fusion performance. Multiple 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,  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
['Kalyan Veeramachaneni', 'Weizhong Yan', 'Kai Goebel', 'Lisa Ann Osadciw']
I am writing a research paper on a idea Improving Classifier Fusion Using Particle Swarm Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Improving Classifier Fusion Using Particle Swarm Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  presents a particle swarm optimization based decision-level fusion scheme for optimizing classifier fusion performance. Multiple 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,  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"}]