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Rule Extraction from Neural Network Using Input Data Ranges Recursively
New Generation Computing ( IF 2.6 ) Pub Date : 2018-11-11 , DOI: 10.1007/s00354-018-0048-0
Manomita Chakraborty , Saroj Kumar Biswas , Biswajit Purkayastha

Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search for algorithms that can generate more accurate and simple rules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network Recursively (ERENNR), with the aim to generate simple and accurate rules. ERENNR algorithm extracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate.

中文翻译:

使用递归输入数据范围从神经网络中提取规则

由于其高精度,神经网络是数据挖掘任务的最佳工具之一。然而,神经网络的缺点之一是它的黑盒性质。这种限制使得神经网络对于许多需要决策过程透明的应用程序毫无用处。已经提出了许多算法通过从神经网络中提取透明规则来克服这个缺点,但研究人员仍在寻找能够生成更准确和简单规则的算法。因此,本文提出了一种名为 Eclectic Rule Extraction from Neural Network Recursively (ERENNR) 的规则提取算法,旨在生成简单准确的规则。ERENNR 算法从单层前馈神经网络中提取符号分类规则。该算法的新颖之处在于其分析网络节点的过程。它基于相对于其输出的输入属性的数据范围来分析隐藏节点,并使用相对于输出类的隐藏节点的输出的逻辑组合来分析输出节点。最后它通过从输出层开始向后进行生成规则集。对于集合中的每条规则,如果该规则满足一定的标准,则重复整个规则抽取过程。该算法通过 11 个基准数据集进行了验证。实验结果表明,生成的规则简单准确。它基于相对于其输出的输入属性的数据范围来分析隐藏节点,并使用相对于输出类的隐藏节点的输出的逻辑组合来分析输出节点。最后它通过从输出层开始向后进行生成规则集。对于集合中的每条规则,如果该规则满足一定的标准,则重复整个规则抽取过程。该算法通过 11 个基准数据集进行了验证。实验结果表明,生成的规则简单准确。它基于相对于其输出的输入属性的数据范围来分析隐藏节点,并使用相对于输出类的隐藏节点的输出的逻辑组合来分析输出节点。最后它通过从输出层开始向后进行生成规则集。对于集合中的每条规则,如果该规则满足一定的标准,则重复整个规则抽取过程。该算法通过 11 个基准数据集进行了验证。实验结果表明,生成的规则简单准确。该算法通过 11 个基准数据集进行了验证。实验结果表明,生成的规则简单准确。该算法通过 11 个基准数据集进行了验证。实验结果表明,生成的规则简单准确。
更新日期:2018-11-11
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