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Computer Science > Machine Learning
Title: Imputation using training labels and classification via label imputation
(Submitted on 28 Nov 2023 (v1), last revised 23 Apr 2024 (this version, v3))
Abstract: Missing data is a common problem in practical settings. Various imputation methods have been developed to deal with missing data. However, even though the label is usually available in the training data, the common practice of imputation usually only relies on the input and ignores the label. In this work, we illustrate how stacking the label into the input can significantly improve the imputation of the input. In addition, we propose a classification strategy that initializes the predicted test label with missing values and stacks the label with the input for imputation. This allows imputing the label and the input at the same time. Also, the technique is capable of handling data training with missing labels without any prior imputation and is applicable to continuous, categorical, or mixed-type data. Experiments show promising results in terms of accuracy.
Submission history
From: Thu Nguyen Ms. [view email][v1] Tue, 28 Nov 2023 15:26:09 GMT (167kb,D)
[v2] Sun, 28 Jan 2024 08:15:55 GMT (1396kb,D)
[v3] Tue, 23 Apr 2024 13:03:10 GMT (2294kb,D)
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