Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework  can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem  create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work  show that by optimizing these thresholds via particle swarm optimization  improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers  can optimize in the same time both the thresholds of sub-ECOC and the parameters C and σ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance.
['Dimitrios Bouzas', 'Nikolaos Arvanitopoulos', 'Anastasios Tefas']
I am writing a research paper on a idea Optimizing subclass discriminant Error Correcting Output Codes using particle swarm optimization
[{"from": "human", "value": "I am writing a research paper on a idea Optimizing subclass discriminant Error Correcting Output Codes 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: Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework  can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem  create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work  show that by optimizing these thresholds via particle swarm optimization  improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers  can optimize in the same time both the thresholds of sub-ECOC and the parameters C and \u03c3 of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance."}]