Publication:
A novel LOF-based ensemble regression tree methodology

dc.contributor.authorÖngelen, Gözde
dc.contributor.authorİnkaya, Tülin
dc.contributor.buuauthorÖngelen, Gözde
dc.contributor.buuauthorİNKAYA, TÜLİN
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentEndüstri Mühendisliği Bölümü
dc.contributor.orcid0000-0002-6260-0162
dc.contributor.researcheridAAH-2155-2021
dc.contributor.researcheridIWT-8849-2023
dc.date.accessioned2024-09-10T07:57:30Z
dc.date.available2024-09-10T07:57:30Z
dc.date.issued2023-06-27
dc.description.abstractWith the emergence of digitilization, numeric prediction has become a prominent problem in various fields including finance, engineering, industry, and medicine. Among several machine learning methods, regression tree is a widely preferred method due to its simplicity, interpretability and robustness. Motivated by this, we introduce a novel ensemble regression tree based methodology, namely LOF-BRT+OR. The proposed methodology is an integrated solution approach with outlier removal, regression tree and ensemble learning. First, irregular data points are removed using local outlier factor (LOF), which measures the degree of being an outlier for each point. Next, a novel regression tree with LOF weighted node model is introduced. In the proposed node model, the weights of the points in the nodes are determined according to their surrounding neighborhood, as a function of LOF values and neighbor ranks. Finally, in order to increase the prediction performance, ensemble learning is adopted. In particular, bootstrap aggregation is used to generate multiple regression trees with LOF weighted node model. The experimental study shows that the proposed methodology yields the best root mean squared error (RMSE) values in five out of nine data sets. Also, the non-parametric tests demonstrate the statistical significance of the proposed approach over the benchmark methods. The proposed methodology can be applicable to various prediction problems.
dc.identifier.doi10.1007/s00521-023-08773-w
dc.identifier.eissn1433-3058
dc.identifier.endpage19463
dc.identifier.issn0941-0643
dc.identifier.issue26
dc.identifier.startpage19453
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08773-w
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-023-08773-w
dc.identifier.urihttps://hdl.handle.net/11452/44464
dc.identifier.volume35
dc.identifier.wos001017560800001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherSpringer
dc.relation.journalNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectClassification
dc.subjectPrediction
dc.subjectSelection
dc.subjectForest
dc.subjectModel
dc.subjectPrediction
dc.subjectRegression tree
dc.subjectEnsemble learning
dc.subjectLocal outlier factor
dc.subjectOutlier removal
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectComputer science, artificial intelligence
dc.subjectComputer science
dc.titleA novel LOF-based ensemble regression tree methodology
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Endüstri Mühendisliği Bölümü
relation.isAuthorOfPublication50789246-3e56-4752-a821-3ae9957be346
relation.isAuthorOfPublication.latestForDiscovery50789246-3e56-4752-a821-3ae9957be346

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