References
- Chung, Eunkyung (2009). A semantic-based feature expansion approach for improving the effectiveness of text categorization by using WordNet. Journal of the Korean Society for information Management, 26(3), 261-278. https://doi.org/10.3743/KOSIM.2009.26.3.261
- KCI(Korea Citation Index) (2022). Data Statistics. National Research Foundation of Korea. Available: https://www.kci.go.kr/kciportal/po/statistics/poStatisticsMain.kci?tab_code=Tab3
- Kim, Pan Jun & Lee, Jae Yun (2012). A study on the reclassification of author keywords for automatic assignment of descriptors. Journal of the Korean Society for Information Management, 29(2), 225-246. https://doi.org/10.3743/KOSIM.2012.29.2.225
- Kim, Pan Jun & Lee, Jae Yun (2018). An experimental study on the performance improvement of automatic classification for the articles of Korean journals based on controlled keywords in international database. Journal of the Korean Library and Information Science, 48-3, 491-510. https://doi.org/10.4275/KSLIS.2014.48.3.491
- Kim, Pan Jun (2006). A study on automatic assignment of descriptors using machine learning. Journal of the Korean Society for Information Management, 23(1), 279-299. https://doi.org/10.3743/KOSIM.2006.23.1.279
- Kim, Pan Jun (2016). An analytical study on performance factors of automatic classification based on machine learning. Journal of the Korean Society for Information Management, 33(2), 33-59. http://dx.doi.org/10.3743/KOSIM.2016.33.2.033
- Kim, Pan Jun (2018). An analytical study on automatic classification of domestic journal articles based on machine learning. Journal of the Korean Society for Information Management, 35(2), 37-62. https://doi.org/10.3743/KOSIM.2018.35.2.037
- Kim, Pan Jun (2019). An analytical study on automatic classification of domestic journal articles using random forest. Journal of the Korean Society for Information Management, 36(2), 37-62. https://doi.org/10.3743/KOSIM.2019.36.2.057
- Kim, Pan Jun (2021a). A study on the characteristics by keyword types in the intellectual structure analysis based on co-word analysis: focusing on overseas open access field. Journal of the Korean Library and Information Science, 55-3, 103-129. http://dx.doi.org/10.4275/KSLIS.2021.55.3.103
- Kim, Pan Jun (2021b). A study on the intellectual structure analysis by keyword type based on profiling: focusing on overseas open access field. Journal of the Korean Library and Information Science, 55-4, 115-140. http://dx.doi.org/10.4275/KSLIS.2021.55.4.115
- Kim, Seon-Wu, Ko, Gun-Woo, Choi, Won-Jun, Jeong, Hee-Seok, Yoon, Hwa-Mook, & Choi, Sung-Pil (2018). Semi-automatic construction of learning set and integration of automatic classification for academic literature in technical sciences. Journal of the Korean Society for Information Management, 35(4), 141-164. http://dx.doi.org/10.3743/KOSIM.2018.35.4.141
- Lee, Jae Yun (2005). An empirical study on improving the performance of text categorization considering the relationships between feature selection criteria and weighting methods. Journal of the Korean Society for Library and Information Science, 39(2), 123-146. http://dx.doi.org/10.4275/kslis.2005.39.2.123
- National Research Foundation of Korea (2016). Academic Research Classification Scheme. Available: https://www.nrf.re.kr/biz/doc/class/view?menu_no=323
- Yuk, Jee Hee & Song, Min (2018). A study of research on methods of automated biomedical document classification using topic modeling and deep learning. Journal of the Korean Society for information Management, 35(2), 63-88. https://doi.org/10.3743/KOSIM.2018.35.2.063
- Abiodun, E. O., Alabdulatif, A., Abiodun, O. I., Alawida, M., Alabdulatif, A., & Alkhawaldeh, R. S. (2021). A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities. Neural Computing & Applications, 33(4), 1-28. https://doi.org/10.1007/s00521-021-06406-8
- Cai, J., Luo, J., Wang, S., & Yang, S. (2018). Feature selection in machine learning: a new perspective. Neurocomputing, 300, 70-79. https://doi.org/10.1016/j.neucom.2017.11.077
- Chandrashekar, G. & Sahin, F. (2014) A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024
- Chang, F., Guo, J., Xu, W., & Yao, K. (2015). A feature selection method to handle imbalanced data in text classification. Journal of Digital Information Management, 13, 169-175. Available: https://www.dline.info/fpaper/jdim/v13i3/v13i3_6.pdf
- Deng, X., Li, Y., Weng, J., & Zhang, J. (2019). Feature selection for text classification: a review. Multimedia Tools and Applications, 78, 3797-3816. https://doi.org/10.1007/s11042-018-6083-5
- Drotar, P., Gazda, J., & Smekal, Z. (2015). An experimental comparison of feature selection methods on two-class biomedical datasets. Computers in Biology and Medicine, 66, 1-10. https://doi.org/10.1016/j.compbiomed.2015.08.010
- Drotar, P., Gazda, M., & Vokorokos, L. (2019). Ensemble feature selection using election methods and ranker clustering. Information Sciences, 480, 365-380. https://doi.org/10.1016/j.ins.2018.12.033
- Forman, G. (2003). An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, 3, 1289-1305. Available: https://www.jmlr.org/papers/volume3/forman03a/forman03a_full.pdf
- Fragoudis, D., Meretakis, D., & Likothanassis, S. (2005). Best terms: an efficient feature-selection algorithm for text categorization. Knowledge and Information Systems, 8(1), 16-33. https://doi.org/10.1007/s10115-004-0177-2
- Gunal, S. (2012). Hybrid feature selection for text classification. Turkish Journal of Electrical Engineering and Computer Science, 20(Sup.2), 1296-1311. Available: https://dergipark.org.tr/en/pub/tbtkelektrik/issue/12058/144170
- Gutkin, M., Shamir, R., & Dror, G. (2009). SlimPLS: a method for feature selection in gene expression-based disease classification. PloS One, 4(7), e6416. https://doi.org/10.1371/journal.pone.0006416
- Guyon, I. & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157-1182. Available: https://dl.acm.org/doi/pdf/10.5555/944919.944968
- Harish, B. & Revanasiddappa, M. (2017). A comprehensive survey on various feature selection methods to categorize text documents. International Journal of Computer Applications, 164, 1-7. http://doi.org/10.5120/ijca2017913711
- Iqbal, M., Abid, M. M., Khalid, M. N., & Manzoor, A. (2020). Review of feature selection methods for text classification. International Journal of Advanced Computer Research, 10(49), 138-152. http://dx.doi.org/10.19101/IJACR.2020.1048037
- Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proceedings of the Fourteenth International Conference on Machine Learning (ICML '97), 143-151. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.45.6977&rep=rep1&type=pdf
- Joachims, T. (2002). Learning to Classify Text Using Support Vector Machines: Methods, theory and algorithms. USA: Kluwer Academic Publishers.
- Kashef, S., Nezamabadi-pour, H., & Nikpour, B. (2018). Multi-label feature selection: a comprehensive review and guiding experiments. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1240. https://doi.org/10.1002/widm.1240
- Kragelj, M. & Kljajic Borstnar, M. (2021). Automatic classification of older electronic texts into the Universal Decimal Classification-UDC. Journal of Documentation, 77(3), 755-776. https://doi.org/10.1108/JD-06-2020-0092
- Kumar, V. & Minz, S. (2014). Feature selection: a literature review. Smart Computing Review, 4(3), 211-229. Available: https://faculty.cc.gatech.edu/~hic/CS7616/Papers/Kumar-Minz-2014.pdf
- Manning, C., Raghavan, P., & Schutze, H. (2008). Introduction to information retrieval. NY, USA: Cambridge University Press.
- Mengle, S. S. R. & Goharian, N. (2009). Ambiguity measure feature-selection algorithm. Journal of the American Society for Information Science & Technology, 60(5), 1037-1050. https://doi.org/10.1002/asi.21023
- Mironczuk, M. & Protasiewicz, J. (2018). A recent overview of the state-of-the-art elements of text classification. Expert Systems with Applications, 106, 36-54. https://doi.org/10.1016/j.eswa.2018.03.058
- Pereira, R. B., Plastino, A., Zadrozny, B., & Merschmann, L. H. (2018). Correlation analysis of performance measures for multi-label classification. Information Processing & Management, 54(3), 359-369. https://doi.org/10.1016/j.ipm.2018.01.002
- Pinheiro, R. H. W., Cavalcanti, G. D. C., & Ren, T. I. (2015). Data-driven global-ranking local feature selection methods for text categorization, Expert Systems with Applications, 42 (4), 1941-1949. https://doi.org/10.1016/j.eswa.2014.10.011
- Pintas, J. T., Fernandes, L. A. F., & Garcia, A. C. B. (2021). Feature selection methods for text classification: a systematic literature review. Artificial Intelligence Review, 54, 6149-6200. https://doi.org/10.1007/s10462-021-09970-6
- Rehman, A., Javed, K., Babri, H. A., & Asim, N. (2018). Selection of the most relevant terms based on a max-min ratio metric for text classification. Expert Systems with Applications, 114, 78-96. https://doi.org/10.1016/j.eswa.2018.07.028
- Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. https://doi.org/10.1016/0306-4573(88)90021-0
- Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1-47. https://doi.org/10.1145/505282.505283
- Talavera, L. (2005). An evaluation of filter and wrapper methods for feature selection in categorical clustering. In International Symposium on Intelligent Data Analysis. Springer, Berlin, Heidelberg, 440-451. https://doi.org/10.1007/11552253_40
- Uysal, A. K. (2016). An improved global feature selection scheme for text classification. Expert Systems with Applications, 43(1), 82-92, https://doi.org/10.1016/j.eswa.2015.08.050
- Venkatesh, B. & Anuradha, J. (2019). A review of feature selection and its methods. Cybernetics and Information Technologies, 19(1), 3-26. https://doi.org/10.2478//cait-2019-0001
- Wang, D., Zhang, H., Liu, R., Liu, X., & Wang, J. (2016). Unsupervised feature selection through gram-Schmidt orthogonalization-A word co-occurrence perspective. Neurocomputing, 173(P3), 845-854. https://doi.org/10.1016/j.neucom.2015.08.038
- Wang, D., Zhang, H., Liu, R., Lv, W., & Wang, D. (2014). t-test feature selection approach based on term frequency for text categorization. Pattern Recognition Letters, 45, 1-10. https://doi.org/10.1016/j.patrec.2014.02.013
- Wu, Y. & Zhang, A. (2004). Feature selection for classifying high-dimensional numerical data. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 2004, 2, 251-258. http://doi.org/10.1109/CVPR.2004.1315171
- Yang, Y. & Pedersen. J. O. (1997). A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, July 08-12, 412-420. Available: http://nyc.lti.cs.cmu.edu/yiming/Publications/yang-icml97.pdf