Parameter-free imputation for imbalance datasets

被引：1
作者：
Theoretical and Empirical Research Group, Department of Computer Science, Chiang Mai University, Chiang Mai [1 ]
50200, Thailand
机构：
[1] Theoretical and Empirical Research Group, Department of Computer Science, Chiang Mai University, Chiang Mai
来源：
Takum, Jintana | 1600年 / Springer Verlag卷 / 8839期
关键词：
Class imbalance; Classification; Imputation; K-nearest neighbours; Parameter-free;
D O I：
10.1007/978-3-319-12823-8_27
中图分类号：
学科分类号：
摘要：
Class imbalance is a problem that aims to improve the accuracy of a minority class, while imputation is a process to replace missing values. Traditionally, class imbalance and imputation problems are considered independently. In addition, filled-in minority-class values that are substituted by traditional methods are not sufficient for imbalance datasets. In this paper, we provide a new parameter-free imputation to operate on imbalance datasets by estimating a random value between the mean of the missing value attribute and a value in this attribute of the closet record instance from the missing value record. Our proposed algorithm ignores mean of instances to avoid an over-fitting problem. Consequently, experimental results on imbalance datasets reveal that our imputation outperforms other techniques, when class imbalance measures are used. © Springer International Publishing Switzerland 2014.
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页码：260 / 267
页数：7
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