Isotonic boosting classification rules

David Conde, Miguel A. Fernández (), Cristina Rueda and Bonifacio Salvador
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David Conde: Universidad de Valladolid
Miguel A. Fernández: Universidad de Valladolid
Cristina Rueda: Universidad de Valladolid
Bonifacio Salvador: Universidad de Valladolid

Advances in Data Analysis and Classification, 2021, vol. 15, issue 2, No 3, 289-313

Abstract: 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.

Keywords: Classification; Boosting; LogitBoost; Additive models; Isotonic regression; TMP; 62H30 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-020-00404-9

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