Abstract
Machine learning is used in this digitizing era so there is an ever-increasing desire for computers to execute human-like jobs. Text classification is rapidly becoming one of machine learning’s most significant tasks. Manually reading the entire book and classifying it based on its genre is, however, a time-consuming operation. As a result, machine learning methods are crucial for classification and in this paper, a book description-based text classification is depicted, where with wide range of wealth of information about the book’s contents. In the proposed system, a variety of classifiers including Multinomial Naive Bayes (MNB), Gradient Boosting (GB), and Random Forest (RF) are employed to categorize the book’s genre. According to the findings, the Naive Bayes Classifier surpasses other two techniques in terms of accuracy, while the other two classifiers provide comparable results.
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Sethy, A., Rout, A.K., Nayak, S.R., Kumar, R. (2024). Book Genre Classification System Through Supervised Learning Technique. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A. (eds) Proceedings of 3rd International Conference on Smart Computing and Cyber Security. SMARTCYBER 2023. Lecture Notes in Networks and Systems, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-97-0573-3_19
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DOI: https://doi.org/10.1007/978-981-97-0573-3_19
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