Knowledge discovery based on neural networks
Communications of the ACM
Determining Hyper-planes to Generate Symbolic Rules
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
WSEAS Transactions on Computers
An Empirical Study on Several Classification Algorithms and Their Improvements
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
On the extraction of decision support rules from fuzzy predictive models
Applied Soft Computing
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A challenging problem in machine learning is to discover the domain rules from a limited number of instances. In a large complex domain, it is often the case that the rules learned by the computer are at most approximate. To address this problem, this paper describes the CFNet which bases its activation function on the certainty factor (CF) model of expert systems. A new analysis on the computational complexity of rule learning in general is provided. A further analysis shows how this complexity can be reduced to a point where the domain rules can be accurately learned by capitalizing on the activation function characteristics of the CFNet. The claimed capability is adequately supported by empirical evaluations and comparisons with related systems