Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. Hover, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. Hover, it is not a straightforward task to understand the network. ,  propose a novel way to incorporate NNs into UCS. The approach offers a good compromise beten compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action,  obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness.  also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.
['Hai Huong Dam', 'Hussein A. Abbass', 'Chris Lokan', 'Xin Yao']
I am writing a research paper on a idea Neural-Based Learning Classifier Systems
[{"from": "human", "value": "I am writing a research paper on a idea Neural-Based Learning Classifier Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: UCS is a supervised learning classifier system that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. Hover, the system may require a large number of rules to cover the input space. Artificial neural networks (NNs), on the other hand, normally provide a more compact representation. Hover, it is not a straightforward task to understand the network. ,  propose a novel way to incorporate NNs into UCS. The approach offers a good compromise beten compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action,  obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness.  also apply negative correlation learning (NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble."}]