References
Forman G. An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, 2003, 3: 1289–1305
Rehman A, Javed K, Babri H A. Feature selection based on a normalized difference measure for text classification. Information Processing & Management, 2017, 53(2): 473–489
Rehman A, Javed K, Babri H A, Asim M N. Selection of the most relevant terms based on a max-min ratio metric for text classification. Expert Systems with Applications, 2018, 114: 78–96
Kim K, Zzang S Y. Trigonometric comparison measure: a feature selection method for text categorization. Data & Knowledge Engineering, 2019, 119: 1–21
Zhou H, Ma Y, Li X. Feature selection based on term frequency deviation rate for text classification. Applied Intelligence, 2021, 51(6): 3255–3274
Zhao Y, Karypis G, Fayyad U. Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery, 2005, 10(2): 141–168
Rehman A, Javed K, Babri H A, Saeed M. Relative discrimination criterion — A novel feature ranking method for text data. Expert Systems with Applications, 2015, 42(7): 3670–3681
Parlak B, Uysal A K. A novel filter feature selection method for text classification: extensive feature selector. Journal of Information Science, 2021, 47(2): 1–20
Belazzoug M, Touahria M, Nouioua F, Brahimi M. An improved sine cosine algorithm to select features for text categorization. Journal of King Saud University-Computer and Information Sciences, 2020, 32(4): 454–464
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This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (19KJA550002).
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Jin, L., Zhang, L. & Zhao, L. Max-difference maximization criterion: a feature selection method for text categorization. Front. Comput. Sci. 17, 171337 (2023). https://doi.org/10.1007/s11704-022-2154-x
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DOI: https://doi.org/10.1007/s11704-022-2154-x