Rule extraction from support vector machines based on consistent region covering reduction
Rule extraction from support vector machines based on consistent region covering reduction
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DOI：
10.1016/j.knosys.2012.12.003
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Peng Fei Zhu;Q. Hu
中科院分区：
文献类型：
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作者：
Peng Fei Zhu;Q. Hu
由于在分类和回归方面的良好性能，支持向量机引起了广泛的关注，并成为近十年来最流行的学习机之一。支持向量机作为一个黑匣子，很难让用户理解和解释。在包括医疗诊断或信用评分在内的许多应用领域中，可理解性和可解释性对于学习模型的实用性非常重要。为了提高支持向量机的可理解性，我们提出了一种通过分析样本分布从支持向量机中提取规则的技术。我们根据分类边界定义样本的一致区域，形成样本空间的一致区域覆盖。然后开发了一种覆盖缩减算法来提取类的紧凑表示，从而导出最小的决策规则集。实验分析表明，与决策树算法和其他支持向量机规则提取方法相比，提取的模型表现良好。
Due to good performance in classification and regression, support vector machines have attracted much attention and become one of the most popular learning machines in last decade. As a black box, the support vector machine is difficult for users’ understanding and explanation. In many application domains including medical diagnosis or credit scoring, understandability and interpretability are very important for the practicability of the learned models. To improve the comprehensibility of SVMs, we propose a rule extraction technique from support vector machines via analyzing the distribution of samples. We define the consistent region of samples in terms of classification boundary, and form a consistent region covering of the sample space. Then a covering reduction algorithm is developed for extracting compact representation of classes, thus a minimal set of decision rules is derived. Experiment analysis shows that the extracted models perform well in comparison with decision tree algorithms and other support vector machine rule extraction methods.