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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
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    References listed on IDEAS

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    1. 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.
    2. 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).
    3. 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).
    4. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    5. Sungyoung Auh & Allan R. Sampson, 2006. "Isotonic logistic discrimination," Biometrika, Biometrika Trust, vol. 93(4), pages 961-972, December.
    6. 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.
    7. 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.
    8. Mary C. Meyer, 2013. "Semi-parametric additive constrained regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 715-730, September.
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