A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification

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dc.contributor.authorOnan A.
dc.contributor.authorKorukoğlu S.
dc.contributor.authorBulut H.
dc.date.accessioned2024-07-22T08:11:17Z
dc.date.available2024-07-22T08:11:17Z
dc.date.issued2016
dc.description.abstractTypically performed by supervised machine learning algorithms, sentiment analysis is highly useful for extracting subjective information from text documents online. Most approaches that use ensemble learning paradigms toward sentiment analysis involve feature engineering in order to enhance the predictive performance. In response, we sought to develop a paradigm of a multiobjective, optimization-based weighted voting scheme to assign appropriate weight values to classifiers and each output class based on the predictive performance of classification algorithms, all to enhance the predictive performance of sentiment classification. The proposed ensemble method is based on static classifier selection involving majority voting error and forward search, as well as a multiobjective differential evolution algorithm. Based on the static classifier selection scheme, our proposed ensemble method incorporates Bayesian logistic regression, naïve Bayes, linear discriminant analysis, logistic regression, and support vector machines as base learners, whose performance in terms of precision and recall values determines weight adjustment. Our experimental analysis of classification tasks, including sentiment analysis, software defect prediction, credit risk modeling, spam filtering, and semantic mapping, suggests that the proposed classification scheme can predict better than conventional ensemble learning methods such as AdaBoost, bagging, random subspace, and majority voting. Of all datasets examined, the laptop dataset showed the best classification accuracy (98.86%). © 2016 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.eswa.2016.06.005
dc.identifier.issn09574174
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/15579
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectAdaptive boosting
dc.subjectAlgorithms
dc.subjectArtificial intelligence
dc.subjectClassifiers
dc.subjectData mining
dc.subjectDiscriminant analysis
dc.subjectEvolutionary algorithms
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectMultiobjective optimization
dc.subjectOptimization
dc.subjectRegression analysis
dc.subjectRisk assessment
dc.subjectSemantics
dc.subjectSupervised learning
dc.subjectText processing
dc.subjectDifferential evolution algorithms
dc.subjectEnsemble learning
dc.subjectLinear discriminant analysis
dc.subjectMulti-objective differential evolution algorithms
dc.subjectSentiment analysis
dc.subjectSoftware defect prediction
dc.subjectSupervised machine learning
dc.subjectWeighted majority voting
dc.subjectClassification (of information)
dc.titleA multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification
dc.typeArticle

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