A hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classification

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dc.contributor.authorOnan, Aytug
dc.contributor.authorKorukoglu, Serdar
dc.contributor.authorBulut, Hasan
dc.date.accessioned2019-10-27T11:07:15Z
dc.date.available2019-10-27T11:07:15Z
dc.date.issued2017
dc.departmentEge Üniversitesien_US
dc.description.abstractSentiment analysis is a critical task of extracting subjective information from online text documents. Ensemble learning can be employed to obtain more robust classification schemes. However, most approaches in the field incorporated feature engineering to build efficient sentiment classifiers. The purpose of our research is to establish an effective sentiment classification scheme by pursuing the paradigm of ensemble pruning. Ensemble pruning is a crucial method to build classifier ensembles with high predictive accuracy and efficiency. Previous studies employed exponential search, randomized search, sequential search, ranking based pruning and clustering based pruning. However, there are tradeoffs in selecting the ensemble pruning methods. In this regard, hybrid ensemble pruning schemes can be more promising. In this study, we propose a hybrid ensemble pruning scheme based on clustering and randomized search for text sentiment classification. Furthermore, a consensus clustering scheme is presented to deal with the instability of clustering results. The classifiers of the ensemble are initially clustered into groups according to their predictive characteristics. Then, two classifiers from each cluster are selected as candidate classifiers based on their pairwise diversity. The search space of candidate classifiers is explored by the elitist Pareto-based multi-objective evolutionary algorithm. For the evaluation task, the proposed scheme is tested on twelve balanced and unbalanced benchmark text classification tasks. In addition, the proposed approach is experimentally compared with three ensemble methods (AdaBoost, Bagging and Random Subspace) and three ensemble pruning algorithms (ensemble selection from libraries of models, Bagging ensemble selection and LibD3C algorithm). Results demonstrate that the consensus clustering and the elitist pareto-based multi-objective evolutionary algorithm can be effectively used in ensemble pruning. The experimental analysis with conventional ensemble methods and pruning algorithms indicates the validity and effectiveness of the proposed scheme. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.ipm.2017.02.008
dc.identifier.endpage833en_US
dc.identifier.issn0306-4573
dc.identifier.issn1873-5371
dc.identifier.issn0306-4573en_US
dc.identifier.issn1873-5371en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage814en_US
dc.identifier.urihttps://doi.org/10.1016/j.ipm.2017.02.008
dc.identifier.urihttps://hdl.handle.net/11454/31998
dc.identifier.volume53en_US
dc.identifier.wosWOS:000401400000005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofInformation Processing & Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEnsemble pruningen_US
dc.subjectConsensus clusteringen_US
dc.subjectMulti-objective evolutionary algorithmen_US
dc.subjectSentiment classificationen_US
dc.titleA hybrid ensemble pruning approach based on consensus clustering and multi-objective evolutionary algorithm for sentiment classificationen_US
dc.typeArticleen_US

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