Abstract
The field of AI and machine learning is constantly evolving, and as the size of data continues to grow, so does the need for accurate and efficient methods of data processing. However, the data is not always perfect, and missing data is becoming common and occurs more frequently. Therefore, imputation techniques, aside from precision, needed to be scalable. For that reason, we examine the performance of Principle Components Analysis Imputation (PCAI) [9], an imputation speeding up framework, for logistic regression. Note that the coefficients of a logistic regression model are usually used for interpretation. Therefore, in addition to examining the improvement in the speed of PCAI, we examine how the coefficients of fitted logistic regression models change when using this imputation speeding-up mechanism. To demonstrate the efficiency of the mentioned method, the model’s performance is compared against frequently used imputation methods on three popular datasets: Fashion MNIST, Gene, and Parkinson. And achieves lower time and better accuracy in most experiments.
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Acknowledgments
We want to thank the University of Science, Vietnam National University in Ho Chi Minh City, and AISIA Research Lab in Vietnam for supporting us throughout this paper.
Funding
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) in Ho Chi Minh City, Vietnam under the grant number DS2023-18-01.
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Nguyen, T.H.T., Le, B., Nguyen, P., Tran, L.G.H., Nguyen, T., Nguyen, B.T. (2023). Principal Components Analysis Based Imputation for Logistic Regression. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_3
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