A neural-network model for learning domain rules based on its activation function characteristics

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.