IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v222y2024icp3-23.html
   My bibliography  Save this article

A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification

Author

Listed:
  • Mohanrasu, S.S.
  • Janani, K.
  • Rakkiyappan, R.

Abstract

In this article, we present a novel approach for text classification feature selection using a pseudo-relation matrix constructed with ridge regression. This approach involves generating a matrix that captures the relationship between features and target variables and then using that matrix to create a multi-criteria decision making (MCDM) problem. We then utilize the Complex Proportional Assessment (COPRAS) method to rank features in order of importance. To assess the performance of our proposed algorithm, we conduct tests on ten real-world text datasets and compare our results to the existing methods. Our findings indicate that the proposed approach outperforms other methods, and we provide statistical significance to support our claims. Our proposed algorithm provides a viable answer for researchers looking for efficient and effective feature selection methods for text data analysis, which can lead to a better understanding and interpretation of the underlying text.

Suggested Citation

  • Mohanrasu, S.S. & Janani, K. & Rakkiyappan, R., 2024. "A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 3-23.
  • Handle: RePEc:eee:matcom:v:222:y:2024:i:c:p:3-23
    DOI: 10.1016/j.matcom.2023.07.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475423003129
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2023.07.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peng, Yi & Kou, Gang & Wang, Guoxun & Shi, Yong, 2011. "FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms," Omega, Elsevier, vol. 39(6), pages 677-689, December.
    2. Liu, Fang & Chen, Ya-Ru & Zhou, Da-Hai, 2023. "A two-dimensional approach to flexibility degree of XOR numbers with application to group decision making," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 267-287.
    3. Hong, Ming & Wang, Heyong, 2021. "Research on customer opinion summarization using topic mining and deep neural network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 88-114.
    4. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
    5. Khalil, Ahmed Mostafa & Zahran, Ahmed Mohamed & Basheer, Rehab, 2023. "A novel diagnosis system for detection of kidney disease by a fuzzy soft decision-making problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 271-305.
    6. Bauer, Frank & Lukas, Mark A., 2011. "Comparingparameter choice methods for regularization of ill-posed problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(9), pages 1795-1841.
    7. Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ibrahim, Rehab Ali & Lu, Songfeng, 2020. "Opposition-based moth-flame optimization improved by differential evolution for feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 168(C), pages 48-75.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kavitha, S. & Satheeshkumar, J. & Amudha, T., 2024. "Multi-label feature selection using q-rung orthopair hesitant fuzzy MCDM approach extended to CODAS," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 148-173.
    2. Janani, K. & Mohanrasu, S.S. & Kashkynbayev, Ardak & Rakkiyappan, R., 2024. "Minkowski distance measure in fuzzy PROMETHEE for ensemble feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 264-295.
    3. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    4. Pang, Jifang & Liang, Jiye, 2012. "Evaluation of the results of multi-attribute group decision-making with linguistic information," Omega, Elsevier, vol. 40(3), pages 294-301.
    5. Tarik Chakkour, 2017. "Some Notes about the Continuous-in-Time Financial Model," Post-Print hal-01584982, HAL.
    6. Adil Yousif & Mohammed Bakri Bashir & Awad Ali, 2024. "An Evolutionary Algorithm for Task Clustering and Scheduling in IoT Edge Computing," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
    7. Daji Ergu & Gang Kou, 2012. "Questionnaire design improvement and missing item scores estimation for rapid and efficient decision making," Annals of Operations Research, Springer, vol. 197(1), pages 5-23, August.
    8. Radu Cristian Alexandru Iacob & Vlad Cristian Monea & Dan Rădulescu & Andrei-Florin Ceapă & Traian Rebedea & Ștefan Trăușan-Matu, 2020. "AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    9. Azzini, Antonia & Cortesi, Nicola & Marrara, Stefania & Topalović, Amir, 2019. "A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 197-208, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    10. Akbari, Sina & Escobedo, Adolfo R., 2023. "Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties," Omega, Elsevier, vol. 119(C).
    11. Xueying Zhang & Qinbao Song, 2015. "A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-30, April.
    12. Junming Yin & Jerry Luo & Susan A. Brown, 2021. "Learning from Crowdsourced Multi-labeling: A Variational Bayesian Approach," Information Systems Research, INFORMS, vol. 32(3), pages 752-773, September.
    13. Peide Liu & Peng Wang, 2017. "Some Improved Linguistic Intuitionistic Fuzzy Aggregation Operators and Their Applications to Multiple-Attribute Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 817-850, May.
    14. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    15. Ginger Saltos & Mihaela Cocea, 2017. "An Exploration of Crime Prediction Using Data Mining on Open Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1155-1181, September.
    16. Babak Daneshvar Rouyendegh & Kazim Topuz & Ali Dag & Asil Oztekin, 2019. "An AHP-IFT Integrated Model for Performance Evaluation of E-Commerce Web Sites," Information Systems Frontiers, Springer, vol. 21(6), pages 1345-1355, December.
    17. Ahmed A. Ewees & Mohammed A. A. Al-qaness & Laith Abualigah & Diego Oliva & Zakariya Yahya Algamal & Ahmed M. Anter & Rehab Ali Ibrahim & Rania M. Ghoniem & Mohamed Abd Elaziz, 2021. "Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    18. N. Thillaigovindan & S. Anita Shanthi & J. Vadivel Naidu, 2016. "New Method for Solving a General Multiple Criteria Decision-Making Problem Under Risk in Fuzzy Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1157-1179, September.
    19. Carmen De Maio & Aurelio Tommasetti & Orlando Troisi & Massimiliano Vesci & Giuseppe Fenza & Vincenzo Loia, 2016. "Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 923-948, September.
    20. Gang Kou & Wenshuai Wu, 2014. "Multi-criteria decision analysis for emergency medical service assessment," Annals of Operations Research, Springer, vol. 223(1), pages 239-254, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:222:y:2024:i:c:p:3-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.