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Selection of Tree‐Biased Classifiers with the Bootstrap 632+ Rule
Author(s) -
Merler Stefano,
Furlanello Cesare
Publication year - 1997
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710390310
Subject(s) - overfitting , weighting , bootstrap aggregating , selection (genetic algorithm) , machine learning , computer science , artificial intelligence , decision tree , tree (set theory) , model selection , statistics , data mining , mathematics , artificial neural network , mathematical analysis , medicine , radiology
This paper introduces a novel model selection procedure for tree‐based classifiers. The method is based on the bootstrap 632+ rule recently proposed by Efron and Tibshirani. The rule allows selecting compact, non‐overfitting classification trees by weighting the contributions of the resubstitution and standard bootstrap estimated error. The proposed method is applied in a medical entomology problem for modeling the risk of parasite presence.