Modified Rule Ensemble Method and its Application for Bioceutical Data

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  • Elastic Net罰則によるルール・アンサンブル法とその応用
  • Elastic Net バッソク ニ ヨル ルール アンサンブルホウ ト ソノ オウヨウ

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Abstract

Ensemble learning methods can improve the prediction accuracy by combining multiple base learners, and are studied in the fields of statistics science and data mining. Since ensemble learning methods construct models of a “black box” nature, the models are difficult to interpret. Friedman and Popescu (2008) proposed the rule ensemble learning method, in which nodes of tree models are used as base learners. The rule ensemble method not only presents the base learner as a production rule, but also gives the response variable an influential measure with rule importance. In the rule ensemble method, base learners are weighted by shrinkage regression using the least absolute shrinkage and selection operator (lasso). However, when some pairs of base learners have high correlation, the lasso method prunes base learners excessively. In this study, we utilized elastic net (Zou and Hastie, 2006) for weighting the base learner to solve the problem of excessive pruning. We called our rule ensemble method the EN-RF method. Furthermore, we developed diagnostic graphics for partial variable importance and partial rule importance. The usefulness of the EN-RF method and its diagnostic graphics were illustrated by a practical example in medical data. In application of medical data, we focused on the characterization of the positive (and/or negative) responder. We found that the EN-RF method shows better performance compared with the existing regression method.

Journal

  • Ouyou toukeigaku

    Ouyou toukeigaku 40 (1), 19-40, 2011

    Japanese Society of Applied Statistics

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