Evaluation of Learning Costs of Rule Evaluation Models Based on Objective Indices to Predict Human Hypothesis Construction Phases
Evaluation of Learning Costs of Rule Evaluation Models Based on Objective Indices to Predict Human Hypothesis Construction Phases
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DOI：
10.1109/grc.2007.155
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发表时间：
2007-11
期刊：
影响因子：
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通讯作者：
H. Abe;S. Tsumoto;M. Ohsaki;Hideto Yokoi;Takahira Yamaguchi
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文献类型：
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作者：
H. Abe;S. Tsumoto;M. Ohsaki;Hideto Yokoi;Takahira Yamaguchi
针对数据挖掘后处理中的迭代规则评估支持方法，提出了一种基于客观指标的规则评估模型的学习代价评估方法。挖掘结果的后处理是数据挖掘过程中的关键过程之一。然而，人类专家很难从大量带有噪声的数据集中获得的数千条规则中发现有价值的知识。为了减少这样的规则评估任务的成本，我们已经开发了规则评估支持方法与规则评估模型，从挖掘的分类规则和评价的客观指标，由人类专家为每个规则学习。为了估计学习成本预测人类利益的客观规则评价指标，我们做了两个案例研究与实际的数据挖掘结果，其中包括不同阶段的人类利益。关于这些结果，我们讨论了学习算法的性能和人类假设构建过程之间的关系。
In this paper, we present an evaluation of learning costs of rule evaluation models based on objective indices for an iterative rule evaluation support method in data mining post-processing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to find out valuable knowledge from several thousands of rules obtained with a large dataset with noises. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To estimate learning costs for predicting human interests with objective rule evaluation indices, we have done the two case studies with actual data mining results, which include different phases of human interests. With regarding to these results, we discuss about the relationship between performances of learning algorithms and human hypothesis construction process.