Inductive learning models with missing values
Inductive learning models with missing values
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DOI：
10.1016/j.mcm.2006.02.013
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发表时间：
2006-11
期刊：
影响因子：
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通讯作者：
I. Fortes;Llanos Mora López;R. Morales;F. Ruiz
中科院分区：
文献类型：
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作者：
I. Fortes;Llanos Mora López;R. Morales;F. Ruiz
本文介绍了一种新的方法来处理归纳学习算法中缺失的属性值。三个基本问题进行了研究：分裂准则，分配值缺失的属性值，和预测的新的意见。给出了分裂准则的形式定义。该定义考虑了缺失的属性值，并推广了经典定义。关于第二个目标，多个值被分配给缺失的属性值使用决策理论的方法。这些多个值中的每一个都将具有相关的置信度和误差参数。error参数测量值与属性的原始值的接近程度或距离。在应用分裂准则之后，获得决策树（从具有或不具有缺失属性值的训练集）。此决策树可用于预测观测的类（有或没有缺失的属性值）。因此，有四个视角。三个角度缺失属性值的研究和实验结果。
In this paper, a new approach to working with missing attribute values in inductive learning algorithms is introduced. Three fundamental issues are studied: the splitting criterion, the allocation of values to missing attribute values, and the prediction of new observations. The formal definition for the splitting criterion is given. This definition takes into account the missing attribute values and generalizes the classical definition. In relation to the second objective, multiple values are assigned to missing attribute values using a decision theory approach. Each of these multiple values will have an associated confidence and error parameter. The error parameter measures how near or how far the value is from the original value of the attribute. After applying a splitting criterion, a decision tree is obtained (from training sets with or without missing attribute values). This decision tree can be used to predict the class of an observation (with or without missing attribute values). Hence, there are four perspectives. The three perspectives with missing attribute values are studied and experimental results are presented.