IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v15y2021i2d10.1007_s11634-020-00404-9.html
   My bibliography  Save this article

Isotonic boosting classification rules

Author

Listed:
  • David Conde

    (Universidad de Valladolid)

  • Miguel A. Fernández

    (Universidad de Valladolid)

  • Cristina Rueda

    (Universidad de Valladolid)

  • Bonifacio Salvador

    (Universidad de Valladolid)

Abstract

In many real classification problems a monotone relation between some predictors and the classes may be assumed when higher (or lower) values of those predictors are related to higher levels of the response. In this paper, we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules. These algorithms are based on theoretical developments that consider isotonic regression. We show the good performance of these procedures not only on simulations, but also on real data sets coming from two very different contexts, namely cancer diagnostic and failure of induction motors.

Suggested Citation

  • David Conde & Miguel A. Fernández & Cristina Rueda & Bonifacio Salvador, 2021. "Isotonic boosting classification rules," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 289-313, June.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:2:d:10.1007_s11634-020-00404-9
    DOI: 10.1007/s11634-020-00404-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-020-00404-9
    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/s11634-020-00404-9?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. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. Yining Chen & Richard J. Samworth, 2016. "Generalized additive and index models with shape constraints," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 729-754, September.
    3. W. Härdle & P. Hall, 1993. "On the backfitting algorithm for additive regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 47(1), pages 43-57, March.
    4. Mary C. Meyer, 2013. "Semi-parametric additive constrained regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 715-730, September.
    5. Conde, David & Fernández, Miguel & Salvador, Bonifacio & Rueda, Cristina, 2015. "dawai: An R Package for Discriminant Analysis with Additional Information," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i10).
    6. de Leeuw, Jan & Hornik, Kurt & Mair, Patrick, 2009. "Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i05).
    7. Sungyoung Auh & Allan R. Sampson, 2006. "Isotonic logistic discrimination," Biometrika, Biometrika Trust, vol. 93(4), pages 961-972, December.
    8. Fernandez, Miguel A. & Rueda, Cristina & Salvador, Bonifacio, 2006. "Incorporating Additional Information to Normal Linear Discriminant Rules," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 569-577, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael Rapp & Johannes Fürnkranz & Eyke Hüllermeier, 2024. "On the efficient implementation of classification rule learning," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 851-892, December.

    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. España, Victor J. & Aparicio, Juan & Barber, Xavier & Esteve, Miriam, 2024. "Estimating production functions through additive models based on regression splines," European Journal of Operational Research, Elsevier, vol. 312(2), pages 684-699.
    2. César Merino-Soto & Gina Chávez-Ventura & Verónica López-Fernández & Guillermo M. Chans & Filiberto Toledano-Toledano, 2022. "Learning Self-Regulation Questionnaire (SRQ-L): Psychometric and Measurement Invariance Evidence in Peruvian Undergraduate Students," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    3. Roberto Burro & Riccardo Sartori & Giulio Vidotto, 2011. "The method of constant stimuli with three rating categories and the use of Rasch models," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(1), pages 43-58, January.
    4. Chang, Hsin-Li & Yang, Cheng-Hua, 2008. "Explore airlines’ brand niches through measuring passengers’ repurchase motivation—an application of Rasch measurement," Journal of Air Transport Management, Elsevier, vol. 14(3), pages 105-112.
    5. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    6. Ivana Bassi & Matteo Carzedda & Enrico Gori & Luca Iseppi, 2022. "Rasch analysis of consumer attitudes towards the mountain product label," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-25, December.
    7. Hua-Hua Chang, 1996. "The asymptotic posterior normality of the latent trait for polytomous IRT models," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 445-463, September.
    8. Curt Hagquist & Raili Välimaa & Nina Simonsen & Sakari Suominen, 2017. "Differential Item Functioning in Trend Analyses of Adolescent Mental Health – Illustrative Examples Using HBSC-Data from Finland," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 10(3), pages 673-691, September.
    9. Thilo J. Ketschau & Janne Kleinhans, 2019. "Concept and Implementation of a Two-Stage Coding Scheme for the Development of Computer-Based Testing (CBT)-Items in Traditional Test Software," J, MDPI, vol. 2(1), pages 1-9, January.
    10. David J. Hessen, 2023. "Fitting and Testing Log-Linear Subpopulation Models with Known Support," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 917-939, September.
    11. Chang, Hsin-Li & Wu, Shun-Cheng, 2008. "Exploring the vehicle dependence behind mode choice: Evidence of motorcycle dependence in Taipei," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 307-320, February.
    12. C. Glas & Anna Dagohoy, 2007. "A Person Fit Test For Irt Models For Polytomous Items," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 159-180, June.
    13. Guido Makransky & Malene Warming Thisgaard & Helen Gadegaard, 2016. "Virtual Simulations as Preparation for Lab Exercises: Assessing Learning of Key Laboratory Skills in Microbiology and Improvement of Essential Non-Cognitive Skills," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-11, June.
    14. Johannes Friedrich & Pengcheng Zhou & Liam Paninski, 2017. "Fast online deconvolution of calcium imaging data," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-26, March.
    15. Jesper Tijmstra & Maria Bolsinova, 2019. "Bayes Factors for Evaluating Latent Monotonicity in Polytomous Item Response Theory Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 846-869, September.
    16. Salzberger, Thomas & Koller, Monika, 2013. "Towards a new paradigm of measurement in marketing," Journal of Business Research, Elsevier, vol. 66(9), pages 1307-1317.
    17. Joakim Wallmark & James O. Ramsay & Juan Li & Marie Wiberg, 2024. "Analyzing Polytomous Test Data: A Comparison Between an Information-Based IRT Model and the Generalized Partial Credit Model," Journal of Educational and Behavioral Statistics, , vol. 49(5), pages 753-779, October.
    18. Fumiko Samejima, 1995. "Acceleration model in the heterogeneous case of the general graded response model," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 549-572, December.
    19. Richard N McNeely & Salissou Moutari & Samuel Arba-Mosquera & Shwetabh Verma & Jonathan E Moore, 2018. "An alternative application of Rasch analysis to assess data from ophthalmic patient-reported outcome instruments," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-32, June.
    20. Francesca DE BATTISTI & Giovanna NICOLINI & Silvia SALINI, 2008. "Methodological overview of Rasch model and application in customer satisfaction survey data," Departmental Working Papers 2008-04, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.

    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:advdac:v:15:y:2021:i:2:d:10.1007_s11634-020-00404-9. 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.