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dc.contributor.authorAli, Shahid
dc.contributor.authorTirumala, Sreenivas Sremath
dc.contributor.authorSarrafzadeh, Hossein
dc.date.accessioned2016-05-11T20:01:04Z
dc.date.available2016-05-11T20:01:04Z
dc.date.issued2015-07
dc.identifier.urihttps://hdl.handle.net/10652/3364
dc.description.abstractIn real world situations every model has some weaknesses and will make errors on training data. Given the fact that each model has certain limitations, the aim of ensemble learning is to supervise their strengths and weaknesses, leading to best possible decision in general. Ensemble based machine learning is a solution of minimizing risk in decision making. Bagging, boosting, stacked generalization and mixture of expert methods are the most popular techniques to construct ensemble systems. For the purpose of combining outputs of class labels, weighted majority voting, behaviour knowledge space and border count methods are used to construct independent classifiers and to achieve diversity among the classifiers which is important in ensemble learning. It was found that an ideal ensemble method should work on the principle of achieving six paramount characteristics of ensemble learning; accuracy, scalability, computational cost, usability, compactness and speed of classification. In addition, the ideal ensemble method would be able to handle large huge image size and long term historical data particularly of spatial and temporal. In this paper we reveal that ensemble models have obtained high acceptability in terms of accuracy than single models. Further, we present an analogy of various ensemble techniques, their applicability, measuring the solution diversity, challenges and proposed methods to overcome these challenges without diverting from the original concepts.en_NZ
dc.language.isoenen_NZ
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_NZ
dc.subjectSupport Vector Machine (SVM)en_NZ
dc.subjectclassifieren_NZ
dc.subjectdiversityen_NZ
dc.subjectensemble systemsen_NZ
dc.subjectalgorithmsen_NZ
dc.subjectmachine learningen_NZ
dc.titleEnsemble learning methods for decision making : status and future prospectsen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)en_NZ
dc.subject.marsden080110 Simulation and Modellingen_NZ
dc.identifier.bibliographicCitationAli, S., Tirumala, S. S., & Sarrafzadeh, A. (2015). Ensemble learning methods for decision making : Status and future prospects. IEEE (Ed.), 13th IEEE International Conference on Machine Learning and Cybernetics (IEEE ICMLC 2015) (pp.1-11)en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionAuckland University of Technologyen_NZ
unitec.publication.spage1en_NZ
unitec.publication.lpage11en_NZ
unitec.publication.title13th IEEE International Conference on Machine Learning and Cybernetics (IEEE ICMLC 2015)en_NZ
unitec.conference.title13th IEEE International Conference on Machine Learning and Cybernetics (IEEE ICMLC 2015)en_NZ
unitec.conference.orgIEEE Systems, Man and Cybernetics Society (SMC)en_NZ
unitec.conference.locationGuangzhou (China)en_NZ
unitec.conference.sdate2015-07-12
unitec.conference.edate2015-07-15
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationAuckland University of Technologyen_NZ
unitec.identifier.roms57914en_NZ
unitec.institution.studyareaComputing


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