Abstract
Appropriate feature selection is an important aspect in the fields of data mining and machine learning. Feature selection reduces data dimensionality and produces simpler classification models that have lower variance. In this paper, we propose a robust feature selection method that produces smaller and yet more effective set of features. The proposed feature selection method leverages association rules to select the effective features for text classification. Our experiment shows that the proposed method outperforms its pears in terms of execution time and classification accuracy.
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Aghbari, Z.A., Saeed, M.M. (2022). Leveraging Association Rules in Feature Selection to Classify Text. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_53
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DOI: https://doi.org/10.1007/978-981-16-3728-5_53
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