IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v222y2024icp264-295.html
   My bibliography  Save this article

Minkowski distance measure in fuzzy PROMETHEE for ensemble feature selection

Author

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

Abstract

A data preprocessing step is necessary to modulate effective and efficient data that can be utilized in numerous data mining and machine learning problems, especially in high-dimensional datasets. Most of the feature selection methods are unstable as different subsets provide various subsets of features giving different classification accuracy. Through ensemble feature selection, higher accuracy can be achieved and it has been verified theoretically and experimentally that diversity among base classifiers further improves its accuracy. In this paper, we model the ensemble feature selection methodology as a decision-making technique. We consider Minkowski distance for fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE) decision making problem as the ensemble feature selection problem. Due to its main advantages in minimizing scalability between criteria and its ease of use for mathematical computations that outrank alternatives, PROMETHEE is an effective tool. To establish the superiority of the proposed methodology, a comparison between existing methodologies has been carried out through various performance metrics.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:222:y:2024:i:c:p:264-295
    DOI: 10.1016/j.matcom.2023.08.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475423003531
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2023.08.027?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. 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.
    2. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    3. 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.
    4. 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.
    5. Bertrand Mareschal & Jean Pierre Brans & Philippe Vincke, 1986. "How to select and how to rank projects: the Prométhée method," ULB Institutional Repository 2013/9307, ULB -- Universite Libre de Bruxelles.
    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. 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.
    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. Guh, Yuh-Yuan, 1997. "Introduction to a new weighting method -- Hierarchy consistency analysis," European Journal of Operational Research, Elsevier, vol. 102(1), pages 215-226, October.
    5. Hajkowicz, Stefan, 2006. "Taking a closer look at multiple criteria analysis and economic evaluation," 2006 Conference (50th), February 8-10, 2006, Sydney, Australia 139785, Australian Agricultural and Resource Economics Society.
    6. Meløn, Mønica García & Aragonés Beltran, Pablo & Carmen González Cruz, M., 2008. "An AHP-based evaluation procedure for Innovative Educational Projects: A face-to-face vs. computer-mediated case study," Omega, Elsevier, vol. 36(5), pages 754-765, October.
    7. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    8. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2018. "σ-µ efficiency analysis: A new methodology for evaluating units through composite indices," MPRA Paper 83569, University Library of Munich, Germany.
    9. Juliana Martins Ruzante & Valerie J. Davidson & Julie Caswell & Aamir Fazil & John A. L. Cranfield & Spencer J. Henson & Sven M. Anders & Claudia Schmidt & Jeffrey M. Farber, 2010. "A Multifactorial Risk Prioritization Framework for Foodborne Pathogens," Risk Analysis, John Wiley & Sons, vol. 30(5), pages 724-742, May.
    10. Mulliner, Emma & Smallbone, Kieran & Maliene, Vida, 2013. "An assessment of sustainable housing affordability using a multiple criteria decision making method," Omega, Elsevier, vol. 41(2), pages 270-279.
    11. Roberto Cervelló Royo & Fernando García García & Francisco Guijarro-Martínez & Ismael Moya-Clemente, 2011. "Housing Ranking: a model of equilibrium between buyers and sellers expectations," ERSA conference papers ersa11p314, European Regional Science Association.
    12. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    13. Qian-Yun Tan & Cui-Ping Wei & Qi Liu & Xiang-Qian Feng, 2016. "The Hesitant Fuzzy Linguistic TOPSIS Method Based on Novel Information Measures," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(05), pages 1-22, October.
    14. Hajkowicz, Stefan & Higgins, Andrew, 2008. "A comparison of multiple criteria analysis techniques for water resource management," European Journal of Operational Research, Elsevier, vol. 184(1), pages 255-265, January.
    15. Ren, Hongbo & Gao, Weijun & Zhou, Weisheng & Nakagami, Ken'ichi, 2009. "Multi-criteria evaluation for the optimal adoption of distributed residential energy systems in Japan," Energy Policy, Elsevier, vol. 37(12), pages 5484-5493, December.
    16. Marco Rogna, 2019. "A First-Phase Screening Device for Site Selection of Large-Scale Solar Plants with an Application to Italy," BEMPS - Bozen Economics & Management Paper Series BEMPS57, Faculty of Economics and Management at the Free University of Bozen.
    17. Vaillancourt, Kathleen & Waaub, Jean-Philippe, 2002. "Environmental site evaluation of waste management facilities embedded into EUGENE model: A multicriteria approach," European Journal of Operational Research, Elsevier, vol. 139(2), pages 436-448, June.
    18. Tomislav Sunko & Marko Mladineo & Mirjana Kovačić & Toni Mišković, 2024. "Multi-Criteria Analysis of Coast Guard Resource Deployment for Improvement of Maritime Safety and Environmental Protection: Case Study of Eastern Adriatic Sea," Sustainability, MDPI, vol. 16(17), pages 1-17, August.
    19. Manuel Casal-Guisande & Alberto Comesaña-Campos & Alejandro Pereira & José-Benito Bouza-Rodríguez & Jorge Cerqueiro-Pequeño, 2022. "A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring," Mathematics, MDPI, vol. 10(3), pages 1-30, February.
    20. Hugo Díaz & Carlos Guedes Soares, 2021. "A Multi-Criteria Approach to Evaluate Floating Offshore Wind Farms Siting in the Canary Islands (Spain)," Energies, MDPI, vol. 14(4), pages 1-18, February.

    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:264-295. 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.