IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v303y2021i1d10.1007_s10479-020-03917-w.html
   My bibliography  Save this article

Solving a class of feature selection problems via fractional 0–1 programming

Author

Listed:
  • Erfan Mehmanchi

    (University of Pittsburgh)

  • Andrés Gómez

    (University of Southern California)

  • Oleg A. Prokopyev

    (University of Pittsburgh)

Abstract

Feature selection is a fundamental preprocessing step for many machine learning and pattern recognition systems. Notably, some mutual-information-based and correlation-based feature selection problems can be formulated as fractional programs with a single ratio of polynomial 0–1 functions. In this paper, we study approaches that ensure globally optimal solutions for these feature selection problems. We conduct computational experiments with several real datasets and report encouraging results. The considered solution methods perform well for medium- and reasonably large-sized datasets, where the existing mixed-integer linear programs from the literature fail.

Suggested Citation

  • Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-020-03917-w
    DOI: 10.1007/s10479-020-03917-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03917-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03917-w?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. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
    2. Anton Kocheturov & Panos M. Pardalos & Athanasia Karakitsiou, 2019. "Massive datasets and machine learning for computational biomedicine: trends and challenges," Annals of Operations Research, Springer, vol. 276(1), pages 5-34, May.
    3. Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
    4. Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2019. "Fractional 0–1 programs: links between mixed-integer linear and conic quadratic formulations," Journal of Global Optimization, Springer, vol. 75(2), pages 273-339, October.
    5. Gintaras Palubeckis, 2004. "Multistart Tabu Search Strategies for the Unconstrained Binary Quadratic Optimization Problem," Annals of Operations Research, Springer, vol. 131(1), pages 259-282, October.
    6. Nimrod Megiddo, 1979. "Combinatorial Optimization with Rational Objective Functions," Mathematics of Operations Research, INFORMS, vol. 4(4), pages 414-424, November.
    7. Robert Tibshirani & Jacob Bien & Jerome Friedman & Trevor Hastie & Noah Simon & Jonathan Taylor & Ryan J. Tibshirani, 2012. "Strong rules for discarding predictors in lasso‐type problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 245-266, March.
    8. Stanislav Busygin & Oleg A. Prokopyev & Panos M. Pardalos, 2005. "Feature Selection for Consistent Biclustering via Fractional 0–1 Programming," Journal of Combinatorial Optimization, Springer, vol. 10(1), pages 7-21, August.
    9. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
    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. Juan S. Borrero & Colin Gillen & Oleg A. Prokopyev, 2017. "Fractional 0–1 programming: applications and algorithms," Journal of Global Optimization, Springer, vol. 69(1), pages 255-282, September.
    2. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    3. Samyukta Sethuraman & Sergiy Butenko, 2015. "The maximum ratio clique problem," Computational Management Science, Springer, vol. 12(1), pages 197-218, January.
    4. Denoyel, Victoire & Alfandari, Laurent & Thiele, Aurélie, 2017. "Optimizing healthcare network design under reference pricing and parameter uncertainty," European Journal of Operational Research, Elsevier, vol. 263(3), pages 996-1006.
    5. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
    6. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    7. Tunjo Perić & Josip Matejaš & Zoran Babić, 2023. "Advantages, sensitivity and application efficiency of the new iterative method to solve multi-objective linear fractional programming problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(3), pages 751-767, September.
    8. Steffen Rebennack & Ashwin Arulselvan & Lily Elefteriadou & Panos M. Pardalos, 2010. "Complexity analysis for maximum flow problems with arc reversals," Journal of Combinatorial Optimization, Springer, vol. 19(2), pages 200-216, February.
    9. Luca Consolini & Marco Locatelli & Jiulin Wang & Yong Xia, 2020. "Efficient local search procedures for quadratic fractional programming problems," Computational Optimization and Applications, Springer, vol. 76(1), pages 201-232, May.
    10. Harald Dyckhoff & Katrin Allen, 1999. "Theoretische Begründung einer Effizienzanalyse mittels Data Envelopment Analysis (DEA)," Schmalenbach Journal of Business Research, Springer, vol. 51(5), pages 411-436, May.
    11. Feng Guo & Liguo Jiao, 2023. "A new scheme for approximating the weakly efficient solution set of vector rational optimization problems," Journal of Global Optimization, Springer, vol. 86(4), pages 905-930, August.
    12. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    13. Hassin, Refael & Sarid, Anna, 2018. "Operations research applications of dichotomous search," European Journal of Operational Research, Elsevier, vol. 265(3), pages 795-812.
    14. Cook, Wade D. & Zhu, Joe, 2007. "Within-group common weights in DEA: An analysis of power plant efficiency," European Journal of Operational Research, Elsevier, vol. 178(1), pages 207-216, April.
    15. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    16. Pursals, Salvador Casadesús & Garzón, Federico Garriga, 2009. "Optimal building evacuation time considering evacuation routes," European Journal of Operational Research, Elsevier, vol. 192(2), pages 692-699, January.
    17. Michael Holzhauser & Sven O. Krumke & Clemens Thielen, 2016. "Budget-constrained minimum cost flows," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1720-1745, May.
    18. Jianzhong Zhang & Zhenhong Liu, 2002. "A General Model of Some Inverse Combinatorial Optimization Problems and Its Solution Method Under l ∞ Norm," Journal of Combinatorial Optimization, Springer, vol. 6(2), pages 207-227, June.
    19. Laurent Alfandari & Alborz Hassanzadeh & Ivana Ljubić, 2021. "An Exact Method for Assortment Optimization under the Nested Logit Model," Working Papers hal-02463159, HAL.
    20. Evgeny Gurevsky & Sergey Kovalev & Mikhail Y. Kovalyov, 2021. "Min-max controllable risk problems," 4OR, Springer, vol. 19(1), pages 93-101, March.

    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:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-020-03917-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.