Evaluating Model Construction Methods with Objective Rule Evaluation Indices to Support Human Experts
Evaluating Model Construction Methods with Objective Rule Evaluation Indices to Support Human Experts
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DOI：
10.1007/11681960_11
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发表时间：
2006-04
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通讯作者：
H. Abe;S. Tsumoto;M. Ohsaki;Takahira Yamaguchi
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文献类型：
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作者：
H. Abe;S. Tsumoto;M. Ohsaki;Takahira Yamaguchi
本文提出了一种基于客观指标的规则评价模型对挖掘结果进行后处理的规则评价支持方法。挖掘结果的后处理是数据挖掘成功的关键问题之一。然而，对于人类专家来说，很难从一个带有噪声的大型数据集中完全评估数千条规则。为了降低规则评价过程的成本，我们开发了基于规则评价模型的规则评价支持方法，该方法由挖掘的分类规则的客观指标和人类专家对每条规则的评价得到。为了评估构建规则评估模型的学习算法的性能，我们将脑膜炎数据挖掘作为一个实际问题进行了案例研究。此外，我们还对来自四个UCI数据集的四个规则集评估了我们的方法。然后，我们展示了我们的规则评估支持方法的有效性。
In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indices. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective indices of mined classification rules and evaluations of a human expert for each rule. To evaluate performances of learning algorithms for constructing rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. In addition, we have also evaluated our method on four rulesets from the four UCI datasets. Then we show the availability of our rule evaluation support method.