An ensemble of optimal trees for class membership probability estimation

Zardad Khan*, Asma Gul, Osama Mahmoud, Miftahuddin Miftahuddin, Aris Perperoglou, Werner Adler, Berthold Lausen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)
381 Downloads (Pure)


Machine learning methods can be used for estimating the class membership probability of an observation. We propose an ensemble of optimal trees in terms of their predictive performance. This ensemble is formed by selecting the best trees from a large initial set of trees grown by random forest. A proportion of trees is selected on the basis of their individual predictive performance on out of-bag observations. The selected trees are further assessed for their collective performance on an independent training data set. This is done by adding the trees one by one starting from the highest predictive tree. A tree is selected for the final ensemble if it increases the predictive performance of the previously combined trees. The proposed method is compared with probability estimation tree, random forest and node harvest on a number of bench mark problems using Brier score as a performance measure. In addition to reducing the number of trees in the ensemble, our method gives better results in most of the cases. The results are supported by a simulation study.

Original languageEnglish
Title of host publicationAnalysis of Large and Complex Data
PublisherSpringer Berlin Heidelberg
Number of pages15
ISBN (Print)9783319252247
Publication statusPublished - 4 Aug 2016
Event2nd European Conference on Data Analysis, ECDA 2014 - Bremen, Germany
Duration: 2 Jul 20144 Jul 2014

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
ISSN (Print)1431-8814


Conference2nd European Conference on Data Analysis, ECDA 2014


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