Multi-objective optimization of ensemble of regression trees using genetic algorithms

Qian Wan, Ranadip Pal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We consider a prediction problem with multiple output responses based on an ensemble of multivariate regression trees. The selection of the optimal ensemble is formulated as a multi-objective optimization problem and solved using genetic algorithms. We illustrate the application of our approach on drug sensitivity prediction problem where the proposed methodology outperforms regular multivariate random forests in terms of correlation coefficients between predicted and experimental sensitivities. We also demonstrate that generating the Pareto-optimal front provides us a choice of ensembles for different optimization objectives.

Original languageEnglish
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1356-1359
Number of pages4
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Conference

Conference2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

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