Paper
21 December 2021 Optimization for overfitting problems in spam email classification based on parameter adjusting
Yusi Wei, Douhao Ma, Juncheng Dong
Author Affiliations +
Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215613 (2021) https://doi.org/10.1117/12.2626447
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China
Abstract
Spam email classification can effectively help us to reject useless information. Different machine learning methods have been proposed to deal with spam email classification problems. However, exploring the key parameters adjusting in the machine learning methods for spam email classification is still insufficient. Therefore, in this paper, we adjusted the key parameters of three classical machine learning methods, including SVM, Random Forest and Logistic Regression in the specific spam email classification tasks. Many tests have been done to evaluate the adjusting operation for different classifiers. The results show that bigger C value of Logistic Regression is, the higher accuracy would be. However, if C value is too big and the model would be overfitting. The amount and the depth of trees may influence the accuracy of Random Forest, where if the amount is bigger and the accuracy would be higher under a limitation. In addition, if the depth of trees is too big, although the accuracy of the model is high, and the model would be overfitting. The Linear kernel function in Logistic Regression has the best performance in the spam email classification task. Our research has a great significance to show how to adjust the parameters for classifiers in the specific classification tasks.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yusi Wei, Douhao Ma, and Juncheng Dong "Optimization for overfitting problems in spam email classification based on parameter adjusting", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215613 (21 December 2021); https://doi.org/10.1117/12.2626447
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KEYWORDS
Data modeling

Machine learning

Feature extraction

Data mining

Feature selection

Lawrencium

Library classification systems

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