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ARTC: feature selection using association rules for text classification

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Abstract

Feature vectors are extracted to represent objects in many classification tasks, such as text classification. Due to the high dimensionality of these raw feature vectors, the classification efficiency and accuracy are reduced. Therefore, reducing the size of feature vectors by selecting the relevant features that better represent the objects is an important aspect in text classification. Feature selection not only reduces the dimensionality of the feature vectors, but also produces more efficient classification models with higher predictive power. In this paper, we propose ARTC, which is an effective feature selection method that is based on the extraction of association rules to classify text documents. The extracted association rules discover the hidden relationships and correlations between the relevant words within the textual documents of a class and a cross different classes. Consequently, each class of documents is represented by a small set of contrasting features that are more effective in text classification. Our experiments show that ARTC outperforms other relevant techniques in terms of classification performance and efficiency.

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Acknowledgements

The authors have published the preliminary results of this work as a short version in a conference [35]. This version includes more detailed analysis of the proposed algorithms, and more experiments and comparisons.

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Authors and Affiliations

  1. Department of Computer Science, Prince Sattam Bin Abdulaziz University, Riyadh, Saudi Arabia

    Mozamel M. Saeed

  2. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Zaher Al Aghbari

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  1. Mozamel M. Saeed
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  2. Zaher Al Aghbari
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Correspondence to Mozamel M. Saeed.

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Zaher Al Aghbari contributed equally to this work.

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Saeed, M.M., Al Aghbari, Z. ARTC: feature selection using association rules for text classification. Neural Comput & Applic 34, 22519–22529 (2022). https://doi.org/10.1007/s00521-022-07669-5

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