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    block this user An-Ping Li

    Research Fellow

    Beijing 100085, P.R.China

    An Application of the Bootstrap 632+ Rule to Ecological Data

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    We applied the novel bootstrap 632+ rule to choose tree-based classifiers trained for modeling the risk of parasite presence in a host population of ungulates. The method is designed to control overfitting: compact classification trees (CART) are selected using a nonlinear combination of the resubstitution error and the standard bootstrap error estimate. Model selection based on the 632+ rule offers a gain over cross-validation for CART models. The tree classifier selected by the new rule for this application favourably compared with standard multivariate GLIM models. Keywords: bootstrap 632+, model selection, classification and regression trees. 1 Introduction Producing the simplest classification model with the smallest prediction error on new observations requires to optimally balance reduction of error on the training material with control of overfitting. An improved bootstrap schema has been recently proposed for model selection in classification problems [1]. The novel bootstrap...

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    Title : An Application of the Bootstrap 632+ Rule to Ecological Data
    Abstract : We applied the novel bootstrap 632+ rule to choose tree-based classifiers trained for modeling the risk of parasite presence in a host population of ungulates. The method is designed to control overfitting: compact classification trees (CART) are selected using a nonlinear combination of the resubstitution error and the standard bootstrap error estimate. Model selection based on the 632+ rule offers a gain over cross-validation for CART models. The tree classifier selected by the new rule for this application favourably compared with standard multivariate GLIM models. Keywords: bootstrap 632+, model selection, classification and regression trees. 1 Introduction Producing the simplest classification model with the smallest prediction error on new observations requires to optimally balance reduction of error on the training material with control of overfitting. An improved bootstrap schema has been recently proposed for model selection in classification problems [1]. The novel bootstrap...
    Subject : unspecified
    Area : Mathematics
    Language : English
    Affiliations
    Url : http://mpa.itc.it:8008/doc/wirn97.ps
    Doi : 10.1.1.27.532

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    An-Ping's Peer Evaluation activity

    Emailed by 1
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    Downloads 725
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