Rule pruning in a fuzzy rule-based classification system
Rule pruning in a fuzzy rule-based classification system
复制标题
基于模糊规则的分类系统中的规则剪枝
DOI：
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发表时间：
2006
期刊：
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通讯作者：
C. Lim
中科院分区：
文献类型：
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作者：
Anas Quteishat;C. Lim
本文提出了一种基于规则的模糊分类系统的规则剪枝策略，通过引入一个基于规则与输入模式相容性的置信度因子来进行规则剪枝，以降低模糊分类系统的复杂度，同时，为了评估剪枝策略的有效性，首先测试了两个基准数据集。最后，以发电厂的真实的传感器测量数据为例，对故障分类问题进行了评估，并对所得结果进行了分析和解释，讨论了规则修剪策略对模糊分类系统的影响。
In this paper, we purpose a rule pruning strategy to reduce the number of rules in a fuzzy rule-based classification system.A confidence factor, which is formulated based on the compatibility of the rules with the input patterns is under deployed for rule pruning.The pruning strategy aims at reducing the complexity of the fuzzy classification system and, at the same time, maintaining the accuracy rate at a good level.To evaluate the effectiveness of the pruning strategy, two benchmark data sets are first tested. Then, a fault classification problem with real senor measurements collected from a power generation plant is evaluated.The results obtained are analyzed and explained, and implications of the proposed rule pruning strategy to the fuzzy classification system are discussed.