Comparing Accuracies of Rule Evaluation Models to Determine Human Criteria on Evaluated Rule Sets
Comparing Accuracies of Rule Evaluation Models to Determine Human Criteria on Evaluated Rule Sets
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DOI：
10.1109/icdmw.2008.49
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
H. Abe;S. Tsumoto
中科院分区：
文献类型：
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作者：
H. Abe;S. Tsumoto
在数据挖掘后处理中，利用客观规则评价指标进行规则选择是从挖掘的模式中发现有价值知识的有效方法之一。然而，指标值与专家标准之间的关系从未得到澄清。在这项研究中，我们比较了随机分类分布和真实人类评估数据集的分类学习算法的准确性。作为一种确定关系的方法，我们使用了规则评价模型，该模型是从由客观规则评价指标和每个规则的评价标签组成的数据集中学习到的。然后，研究结果表明，在平衡随机分类分布下，有和没有人类专家标准的分类学习算法的准确率是不同的。针对这些结果，我们可以考虑一种利用多种学习算法的准确率来区分随机评估规则的方法。
In data mining post-processing, rule selection using objective rule evaluation indices is one of a useful method to find out valuable knowledge from mined patterns. However, the relationship between an index value and experts' criteria has never been clarified. In this study, we have compared the accuracies of classification learning algorithms for datasets with randomized class distributions and real human evaluations. As a method to determine the relationship, we used rule evaluation models, which are learned from a dataset consisting of objective rule evaluation indices and evaluation labels for each rule. Then, the results show that accuracies of classification learning algorithms with/without criteria of human experts are different on a balanced randomized class distribution. With regarding to the results, we can consider about a way to distinguish randomly evaluated rules using the accuracies of multiple learning algorithms.