Research Article
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Year 2023, Volume: 10 Issue: 4, 472 - 486, 31.12.2023

Rasim Çekik Mahmut Kaya

https://doi.org/10.54287/gujsa.1379024
Cited By: 1

Abstract

References

  • Aggarwal, C., & Zhai, C. (2012). A survey of text classification algorithms. In: C. C. Aggarwal, & C Zhai (Eds.), Mining text data (pp. 163-222). https://doi.org/10.1007/978-1-4614-3223-4_6
  • Alberto, T. C., Lochter, J. V., & Almeida, T. A. (2015, December 9-11). Tubespam: Comment spam filtering on youtube. In: Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, Florida. https://doi.org/10.1109/ICMLA.2015.37
  • Bermejo, P., De la Ossa, L., G´amez, J., & Puerta, J. (2012). Fast wrapper feature subset selection in highdimensional datasets by means of filter re-ranking. Knowledge Based Systems, 25(1), 35-44. https://doi.org/10.1016/j.knosys.2011.01.015
  • Cekik, R., & Uysal, A. K. (2020). A novel filter feature selection method using rough set for short text data. Expert Systems with Applications, 160, 113691. https://doi.org/10.1016/j.eswa.2020.113691
  • Cekik, R., & Uysal, A. K. (2022). A new metric for feature selection on short text datasets. Concurrency and Computation: Practice and Experience, 34(13), e6909. https://doi.org/10.1002/cpe.6909
  • Chen, J., Huang, H., Tian, S., & Qu, Y. (2009). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432-5435. https://doi.org/10.1016/j.eswa.2008.06.054
  • Chou, C., Sinha, A., & Zhao, H. (2010). A hybrid attribute selection approach for text classification. Journal of the Association for Information Systems, 11(9), 491. https://doi.org/10.17705/1jais.00236
  • Ghareb, A., Bakar, A., & Hamdan, A. (2016). Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Systems with Applications, 49, 31-47. https://doi.org/10.1016/j.eswa.2015.12.004
  • Gutlein, M., Frank, E., Hall, M., & Karwath, A. (2009, March 30 - April 2). Large-scale attribute selection using wrappers. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, (pp. 332-339), Nashville, TN. https://doi.org/10.1109/CIDM.2009.4938668
  • Joachims, T. (1998, April 21-23). Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the European conference on machine learning (pp. 137-142). Berlin, Heidelberg. https://doi.org/10.1007/BFb0026683
  • Kaya, M., Bi̇lge, H. Ş., & Yildiz, O. (2013, April 24-26). Feature selection and dimensionality reduction on gene expressions. In: Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4), Haspolat. https://doi.org/10.1109/siu.2013.6531476
  • Kaya, M., & Bi̇lge, H. Ş. (2016, May 16-19). A hybrid feature selection approach based on statistical and wrapper methods. In: Proceedings of the 24th Signal Processing and Communication Application Conference (SIU) (pp. 2101-2104), Zonguldak. https://doi.org/10.1109/SIU.2016.7496186
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150. https://doi.org/10.3390/info10040150
  • Labani, M., Moradi, P., Ahmadizar, F., & Jalili, M. (2018). A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence, 70, 25-37. https://doi.org/10.1016/j.engappai.2017.12.014
  • Nuruzzaman, M. T., Lee, C., & Choi, D. (2011, August 31 - September 2). Independent and Personal SMS Spam Filtering. In: Proceedings of the IEEE 11th International Conference on Computer and Information Technology, (pp. 429-435), Paphos. https://doi.org/10.1109/CIT.2011.23
  • Ogura, H., Amano, H., & Kondo, M. (2009). Feature selection with a measure of deviations from Poisson in text categorization. Expert Systems with Applications, 36(3), 6826-6832. https://doi.org/10.1016/j.eswa.2008.08.006
  • Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics & Systems, 29(7), 661-688. https://doi.org/10.1080/019697298125470
  • Pearson, E. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika, 17(3-4), 388-442. https://doi.org/10.1093/biomet/17.3-4.388
  • Rehman, A., Javed, K., Babri, H. A., & Saeed, M. (2015). Relative discrimination criterion–A novel feature ranking method for text data. Expert Systems with Applications, 42(7), 3670-3681. https://doi.org/10.1016/j.eswa.2014.12.013
  • Rehman, A., Javed, K., & Babri, H. A. (2017). Feature selection based on a normalized difference measure for text classification. Information Processing & Management, 53(2), 473-489. https://doi.org/10.1016/j.ipm.2016.12.004
  • Rehman, A., Javed, K., Babri, H. A., & Asim, M. 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
  • Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y., & Wang, Z. (2007). A novel feature selection algorithm for text categorization. Expert Systems with Applications, 33(1), 1-5. https://doi.org/10.1016/j.eswa.2006.04.001
  • Shang, C., Li, M., Feng, S., Jiang, Q., & Fan, J. (2013). Feature selection via maximizing global information gain for text classification. Knowledge-Based Systems, 54, 298-309. https://doi.org/10.1016/j.knosys.2013.09.019
  • Sharmin, S., Shoyaib, M., Ali, A. A., Khan, M. A., & Chae, O. (2019). Simultaneous feature selection and discretization based on mutual information. Pattern Recognition, 91, 162-174. https://doi.org/10.1016/j.patcog.2019.02.016
  • Şenol, A. (2023). Comparison of Performance of Classification Algorithms Using Standard Deviation-based Feature Selection in Cyber Attack Datasets. International Journal of Pure and Applied Sciences, 9(1), 209-222. https://doi.org/10.29132/ijpas.1278880
  • Uysal, A. K., & Gunal, S. (2012). A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, 226-235. https://doi.org/10.1016/j.knosys.2012.06.005
  • Wang, H., & Hong, M. (2019). Supervised Hebb rule based feature selection for text classification. Information Processing & Management, 56(1), 167-191. https://doi.org/10.1016/j.ipm.2018.09.004
  • Wang, S., Li, D., Wei, Y., & Li, H. (2009). A feature selection method based on fisher’s discriminant ratio for text sentiment classification. In: Proceedings of the International Conference on Web Information Systems and Mining (pp. 88-97). Berlin. https://doi.org/10.1007/978-3-642-05250-7_10
  • Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapni, V. (2001). Feature selection for SVMs. Advances in neural information processing systems, Denver, CO (pp. 668-674).
  • Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. 14th International Conference on Machine Learning, Nashville, USA, (pp. 412-420).
  • Zhang, Q., Xie, Q., & Wang, G. (2016). A survey on rough set theory and its applications. CAAI Transactions on Intelligence Technology, 1(4), 323-333. https://doi.org/10.1016/j.trit.2016.11.001

A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification

Year 2023, Volume: 10 Issue: 4, 472 - 486, 31.12.2023

Rasim Çekik Mahmut Kaya

https://doi.org/10.54287/gujsa.1379024
Cited By: 1

Abstract

In text classification, taking words in text documents as features creates a very high dimensional feature space. This is known as the high dimensionality problem in text classification. The most common and effective way to solve this problem is to select an ideal subset of features using a feature selection approach. In this paper, a new feature selection approach called Rough Information Gain (RIG) is presented as a solution to the high dimensionality problem. Rough Information Gain extracts hidden and meaningful patterns in text data with the help of Rough Sets and computes a score value based on these patterns. The proposed approach utilizes the selection strategy of the Information Gain Selection (IG) approach when pattern extraction is completely uncertain. To demonstrate the performance of the Rough Information Gain in the experimental studies, the Micro-F1 success metric is used to compare with Information Gain Selection (IG), Chi-Square (CHI2), Gini Coefficient (GI), Discriminative Feature Selector (DFS) approaches. The proposed Rough Information Gain approach outperforms the other methods in terms of performance, according to the results.

Keywords

Feature Selection Text Classification Rough Set Dimensionality Reduction

References

  • Aggarwal, C., & Zhai, C. (2012). A survey of text classification algorithms. In: C. C. Aggarwal, & C Zhai (Eds.), Mining text data (pp. 163-222). https://doi.org/10.1007/978-1-4614-3223-4_6
  • Alberto, T. C., Lochter, J. V., & Almeida, T. A. (2015, December 9-11). Tubespam: Comment spam filtering on youtube. In: Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, Florida. https://doi.org/10.1109/ICMLA.2015.37
  • Bermejo, P., De la Ossa, L., G´amez, J., & Puerta, J. (2012). Fast wrapper feature subset selection in highdimensional datasets by means of filter re-ranking. Knowledge Based Systems, 25(1), 35-44. https://doi.org/10.1016/j.knosys.2011.01.015
  • Cekik, R., & Uysal, A. K. (2020). A novel filter feature selection method using rough set for short text data. Expert Systems with Applications, 160, 113691. https://doi.org/10.1016/j.eswa.2020.113691
  • Cekik, R., & Uysal, A. K. (2022). A new metric for feature selection on short text datasets. Concurrency and Computation: Practice and Experience, 34(13), e6909. https://doi.org/10.1002/cpe.6909
  • Chen, J., Huang, H., Tian, S., & Qu, Y. (2009). Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3), 5432-5435. https://doi.org/10.1016/j.eswa.2008.06.054
  • Chou, C., Sinha, A., & Zhao, H. (2010). A hybrid attribute selection approach for text classification. Journal of the Association for Information Systems, 11(9), 491. https://doi.org/10.17705/1jais.00236
  • Ghareb, A., Bakar, A., & Hamdan, A. (2016). Hybrid feature selection based on enhanced genetic algorithm for text categorization. Expert Systems with Applications, 49, 31-47. https://doi.org/10.1016/j.eswa.2015.12.004
  • Gutlein, M., Frank, E., Hall, M., & Karwath, A. (2009, March 30 - April 2). Large-scale attribute selection using wrappers. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, (pp. 332-339), Nashville, TN. https://doi.org/10.1109/CIDM.2009.4938668
  • Joachims, T. (1998, April 21-23). Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the European conference on machine learning (pp. 137-142). Berlin, Heidelberg. https://doi.org/10.1007/BFb0026683
  • Kaya, M., Bi̇lge, H. Ş., & Yildiz, O. (2013, April 24-26). Feature selection and dimensionality reduction on gene expressions. In: Proceedings of the 21st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4), Haspolat. https://doi.org/10.1109/siu.2013.6531476
  • Kaya, M., & Bi̇lge, H. Ş. (2016, May 16-19). A hybrid feature selection approach based on statistical and wrapper methods. In: Proceedings of the 24th Signal Processing and Communication Application Conference (SIU) (pp. 2101-2104), Zonguldak. https://doi.org/10.1109/SIU.2016.7496186
  • Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: A survey. Information, 10(4), 150. https://doi.org/10.3390/info10040150
  • Labani, M., Moradi, P., Ahmadizar, F., & Jalili, M. (2018). A novel multivariate filter method for feature selection in text classification problems. Engineering Applications of Artificial Intelligence, 70, 25-37. https://doi.org/10.1016/j.engappai.2017.12.014
  • Nuruzzaman, M. T., Lee, C., & Choi, D. (2011, August 31 - September 2). Independent and Personal SMS Spam Filtering. In: Proceedings of the IEEE 11th International Conference on Computer and Information Technology, (pp. 429-435), Paphos. https://doi.org/10.1109/CIT.2011.23
  • Ogura, H., Amano, H., & Kondo, M. (2009). Feature selection with a measure of deviations from Poisson in text categorization. Expert Systems with Applications, 36(3), 6826-6832. https://doi.org/10.1016/j.eswa.2008.08.006
  • Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics & Systems, 29(7), 661-688. https://doi.org/10.1080/019697298125470
  • Pearson, E. (1925). Bayes’ theorem, examined in the light of experimental sampling. Biometrika, 17(3-4), 388-442. https://doi.org/10.1093/biomet/17.3-4.388
  • Rehman, A., Javed, K., Babri, H. A., & Saeed, M. (2015). Relative discrimination criterion–A novel feature ranking method for text data. Expert Systems with Applications, 42(7), 3670-3681. https://doi.org/10.1016/j.eswa.2014.12.013
  • Rehman, A., Javed, K., & Babri, H. A. (2017). Feature selection based on a normalized difference measure for text classification. Information Processing & Management, 53(2), 473-489. https://doi.org/10.1016/j.ipm.2016.12.004
  • Rehman, A., Javed, K., Babri, H. A., & Asim, M. 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
  • Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y., & Wang, Z. (2007). A novel feature selection algorithm for text categorization. Expert Systems with Applications, 33(1), 1-5. https://doi.org/10.1016/j.eswa.2006.04.001
  • Shang, C., Li, M., Feng, S., Jiang, Q., & Fan, J. (2013). Feature selection via maximizing global information gain for text classification. Knowledge-Based Systems, 54, 298-309. https://doi.org/10.1016/j.knosys.2013.09.019
  • Sharmin, S., Shoyaib, M., Ali, A. A., Khan, M. A., & Chae, O. (2019). Simultaneous feature selection and discretization based on mutual information. Pattern Recognition, 91, 162-174. https://doi.org/10.1016/j.patcog.2019.02.016
  • Şenol, A. (2023). Comparison of Performance of Classification Algorithms Using Standard Deviation-based Feature Selection in Cyber Attack Datasets. International Journal of Pure and Applied Sciences, 9(1), 209-222. https://doi.org/10.29132/ijpas.1278880
  • Uysal, A. K., & Gunal, S. (2012). A novel probabilistic feature selection method for text classification. Knowledge-Based Systems, 36, 226-235. https://doi.org/10.1016/j.knosys.2012.06.005
  • Wang, H., & Hong, M. (2019). Supervised Hebb rule based feature selection for text classification. Information Processing & Management, 56(1), 167-191. https://doi.org/10.1016/j.ipm.2018.09.004
  • Wang, S., Li, D., Wei, Y., & Li, H. (2009). A feature selection method based on fisher’s discriminant ratio for text sentiment classification. In: Proceedings of the International Conference on Web Information Systems and Mining (pp. 88-97). Berlin. https://doi.org/10.1007/978-3-642-05250-7_10
  • Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., & Vapni, V. (2001). Feature selection for SVMs. Advances in neural information processing systems, Denver, CO (pp. 668-674).
  • Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. 14th International Conference on Machine Learning, Nashville, USA, (pp. 412-420).
  • Zhang, Q., Xie, Q., & Wang, G. (2016). A survey on rough set theory and its applications. CAAI Transactions on Intelligence Technology, 1(4), 323-333. https://doi.org/10.1016/j.trit.2016.11.001
There are 31 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Information and Computing Sciences
Authors

Rasim Çekik 0000-0002-7820-413X

Mahmut Kaya 0000-0002-7846-1769

Early Pub Date December 12, 2023
Publication Date December 31, 2023
Submission Date October 20, 2023
Acceptance Date November 17, 2023
Published in Issue Year 2023 Volume: 10 Issue: 4

Cite

APA Çekik, R., & Kaya, M. (2023). A New Feature Selection Metric Based on Rough Sets and Information Gain in Text Classification. Gazi University Journal of Science Part A: Engineering and Innovation, 10(4), 472-486. https://doi.org/10.54287/gujsa.1379024

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https://doi.org/10.1002/adts.202301113
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