摘要
从功能性观点出发 ,提出了一种基于统计的神经网络规则抽取方法 .该方法利用统计技术对抽取出的规则进行评价 ,使其可以较好地覆盖示例空间 .采用独特的连续属性处理方式 ,降低了离散化处理的主观性和复杂度 .采用优先级规则形式 ,不仅使得规则表示简洁、紧凑 ,而且还免除了规则应用时所需要的一致性处理 .该方法不依赖于具体的网络结构和训练算法 ,可以方便地应用于各种分类器型神经网络 .实验表明 ,利用该方法可以抽取出可理解性好 ,简洁、紧凑 ,保真度高的符号规则 .
In this paper, from the functional point of view, a statistics based approach for rule extraction from trained neural networks is proposed. This approach introduces statistical technique to evaluate extracted rules so that the rule set could well cover the instance space. It deals with continuous attributes in a unique way so that the subjectivity and complexity of discretization are lowered. It adopts ordered rule representation so that not only the rules have concise appearance but also the consistency process could be released when the rules are used. Moreover, this approach is independent of the architecture and training algorithm so that it could be easily applied to diversified neural classifiers. Experimental results show that the symbolic rules extracted via this approach are comprehensible, compact, and with high fidelity.
出处
《软件学报》
EI
CSCD
北大核心
2001年第2期263-269,共7页
Journal of Software
基金
国家自然科学基金!资助项目 (6 9875 0 0 6 )
江苏省自然科学基金!资助项目 (BK990 36 )&&
