Marcelino, C. G. and Leite, G. M. C. and Celes, P. and Pedreira, C. E. (2022) Missing Data Analysis in Regression. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514
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Abstract
Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms. We investigated the actual effects of missing data for regression by analyzing its impact in several publicly available databases implementing popular algorithms like Decision Tree, Random Forests, Adaboost, K-Nearest Neighbors, Support Vector Machines, and Neural Networks. Our goal is to offer a systematic view of how missing data may affect regression results. After exhaustive simulation analyzing eight public datasets from UCI and KEEL (Abalone, Arfoil, Bike, California, Compactiv, Mortage, Wankara and Wine), we concluded that the effect of missing data may be significant. The results obtained showed that K-Nearest Neighbors works better than others in the regression of data that has missing data.
Item Type: | Article |
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Subjects: | Open STM Article > Computer Science |
Depositing User: | Unnamed user with email support@openstmarticle.com |
Date Deposited: | 21 Jun 2023 08:11 |
Last Modified: | 04 Jun 2024 11:50 |
URI: | http://asian.openbookpublished.com/id/eprint/1076 |