Quadratic programming algorithms for ensemble models. (26th December 2012)

• Record Type: Journal Article
• Authors: Xu, Jie; Gray, J. Brian
• Is Part Of: Wiley interdisciplinary reviews. Volume 5:Number 1(2013)
• Page Start: 41
• Page End: 47
• Publication Date: 2012-12-26
• Subjects: bagging -- boosting -- decision tree -- ensemble pruning -- predictive model -- random forests; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1237
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: Ensemble models, such as bagging, random forests, and boosting, have better predictive accuracy than single classifiers. These ensembles typically consist of hundreds of single classifiers, which make future predictions and model interpretation much more difficult than for single classifiers. Recently, research efforts have been directed toward improving ensembles by reducing their size while increasing or maintaining their predictive accuracy. In this article, we review recently proposed methods based on quadratic programming techniques for accomplishing these goals. WIREs Comput Stat 2013, 5:41–47. doi: 10.1002/wics.1237 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Algorithms and Computational Methods > Quadratic and Nonlinear Programming