Abstract
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.
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Takum, J., Bunkhumpornpat, C. (2014). Parameter-Free Imputation for Imbalance Datasets. In: Tuamsuk, K., Jatowt, A., Rasmussen, E. (eds) The Emergence of Digital Libraries – Research and Practices. ICADL 2014. Lecture Notes in Computer Science, vol 8839. Springer, Cham. https://doi.org/10.1007/978-3-319-12823-8_27
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DOI: https://doi.org/10.1007/978-3-319-12823-8_27
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