KDD中规则提取的收敛网络方法及其应用

提出一种新的基于神经网络的规则提取方法.提出的网络由一个主网络及其映射网络组成,具有二次收敛过程.通过主网络的学习(第1次收敛)完成知识学习和网络构造，在此基础上构造了其网络映射,通过该映射网络的收敛过程实现规则的提取.该方法在规则提取时无须遍历解空间,从而很好地提高了搜索效率,降低了计算复杂度.同时,还提出估计规则数下限的信度差方法.模拟实验和应用实验也验证了所提出方法的有效性和正确性.

Convergent Network Approach for Rule Extraction in KDD and Its Applications

A novel neural network based rule extraction method is proposed in this paper. This method consists of a primary network and its corresponding mapping network, which includes twice convergent processes. The knowledge acquisition and network construction of the method are fulfilled by the first convergence of the primary network. Here by a mapping network corresponding to the converged primary network is created whose convergence is capable of realizing the rule extraction. Since there is no need of enumerating the overall space of solutions for this method to extract rules, therefore the searching efficiency is greatly increased and the computation complexity is dramatically reduced. Meanwhile, a stop criterion of rule extraction in terms of difference of belief degree is also proposed in this paper. A lot of simulation experiments and practical applications illustrate and verify the validity and correctness of the proposed method.