Association Rule-based Classifier Using Artificial Missing Values
Association Rule-based Classifier Using Artificial Missing Values
复制标题
使用人工缺失值的基于关联规则的分类器
DOI：
10.1007/978-3-319-62701-4_5
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Takashi Hanioka
中科院分区：
文献类型：
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作者：
Kaoru Shimada;Takaaki Arahira;Takashi Hanioka
本文提出了一种基于规则的分类方法，该方法利用人工缺失值来提高医疗数据分析的有效性和精确度。为了避免连续变量离散化时遇到的尖锐边界问题，我们采用了人为的缺失值。在离散化中，我们将边界附近的属性值视为缺失值。我们对所提出的基于缺失值的人工分类方法的性能进行了评估，并使用医学数据进行了实验，结果表明该方法是有效的。该方法可以减少构建分类器所需的规则数量。它还可以在基于规则的分类器中控制假阳性和真阳性之间的关系。
In this paper, we propose a rule-based classification method that uses artificial missing values to improve the effectiveness and precision of medical data analysis. We apply artificial missing values to avoid the sharp boundary problem encountered when discretizing continuous variables. In discretization, we treat attribute values near the boundary as missing values. We evaluated the performance of the proposed artificial missing value-based classification method and our experimental results using medical data show this method to be effective for classification. The proposed method can reduce the number of rules required to build a classifier. It may also be able to control the relation between a false positive and true positive in rule-based classifiers.