Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction

dc.contributor.author Shahhosseini, Mohsen
dc.contributor.author Hu, Guiping
dc.contributor.author Pham, Hieu
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2019-09-20T03:02:34.000
dc.date.accessioned 2020-06-30T04:46:42Z
dc.date.available 2020-06-30T04:46:42Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.embargo 2018-01-01
dc.date.issued 2019-01-01
dc.description.abstract <p>Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known housing datasets have been selected as case studies: Boston housing and Ames housing. The results demonstrate that our designed ensembles can be very competitive in predicting the house prices in both Boston and Ames datasets.</p>
dc.description.comments <p>This is a manuscript of the conference proceeding Shahhosseini, Mohsen, Guiping Hu, and Hieu Pham. "Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction." 2019 INFORMS Conference on Service Science (CSS2019). Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_conf/185/
dc.identifier.articleid 1187
dc.identifier.contextkey 15001599
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_conf/185
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44262
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_conf/185/2019_ShahhosseiniMohsen_OptimizingEnsemble.pdf|||Fri Jan 14 21:43:07 UTC 2022
dc.subject.disciplines Operational Research
dc.subject.keywords Machine Learning
dc.subject.keywords Optimal Ensemble
dc.subject.keywords Bias-Variance Trade off
dc.subject.keywords House Price Prediction
dc.title Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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