Abstract
The growth of data has seen a huge upheaval of messages for various business purposes, engendering the need for spam classification to be prioritized as that of paramount importance. In this paper, A novel approach to spam classification using the algorithms of passive-aggressive spectrum with genetic optimization is proposed. The paper discusses application of such online learning algorithm to classify spam and do a comparative study with existing approaches to spam classification. The results demonstrate the robustness of the algorithm selected and provide a study of the effect of hyperparameters on classification. The Dataset used for classification study is public SMS spam dataset, Spam review and twitter spam datasets, 80% of each dataset was used for training and 20% for testing.The proposed algorithm outperforms standard benchmark algorithms in terms of accuracy,precision, recall scores.
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Data availability
All the data utilized in the paper is publicly available on Kaggle datasets.
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Naravajhula, P., Naravajula, A. Spam Classification: Genetically Optimized Passive-Aggressive Approach. SN COMPUT. SCI. 4, 93 (2023). https://doi.org/10.1007/s42979-022-01517-y
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DOI: https://doi.org/10.1007/s42979-022-01517-y