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dc.contributor.authorMaadi, M
dc.contributor.authorAickelin, U
dc.contributor.authorKhorshidi, HA
dc.date.accessioned2021-01-27T00:15:00Z
dc.date.available2021-01-27T00:15:00Z
dc.date.issued2020-12-01
dc.identifier.citationMaadi, M., Aickelin, U. & Khorshidi, H. A. (2020). An interval-based aggregation approach based on Bagging and Interval Agreement Approach in ensemble learning. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 00, pp.692-699. IEEE. https://doi.org/10.1109/SSCI47803.2020.9308611.
dc.identifier.isbn9781728125473
dc.identifier.urihttp://hdl.handle.net/11343/258840
dc.description.abstractThe main aim in ensemble learning is using multiple classifiers rather than one classifier to aggregate classifiers' outputs for more accurate classification. Generating an ensemble classifier generally is composed of three steps: selecting a base classifier, applying a sampling strategy to generate different simple classifiers and aggregating the classifiers' outputs. This paper focuses on the classifiers' outputs aggregation step in ensemble learning and presents a new interval-based aggregation approach using Bagging and Interval Agreement Approach (IAA). Bagging is an ensemble learning approach to generate ensembles of classifiers by manipulation of the training data set and IAA is an aggregation approach in decision making which was introduced to combine decision makers' opinions when they present their opinions by intervals. In this paper, we use Bagging approach to generate uncertainty intervals for simple classifiers in ensemble learning and implement IAA to aggregate the intervals with the aim of capturing uncertainty. In fact, we design some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and leads to more accurate classification. We compare the results of implementing the proposed method to the majority vote, as the most commonly used aggregation function in ensemble learning, for 10 medical data sets. The results show the better performance of the proposed interval-based aggregation approach in binary classification when it comes to ensemble learning. The Bayesian signed-rank test confirms the competency of our proposed approach in this research.
dc.publisherIEEE
dc.sourceIEEE Symposium Series on Computational Intelligence (SSCI)
dc.titleAn interval-based aggregation approach based on Bagging and Interval Agreement Approach in ensemble learning
dc.typeConference Paper
dc.identifier.doi10.1109/SSCI47803.2020.9308611
melbourne.source.title2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
melbourne.source.volume00
melbourne.source.pages692-699
melbourne.elementsid1491372
melbourne.contributor.authorAickelin, Uwe
melbourne.contributor.authorAkbarzadeh Khorshidi, Hadi
melbourne.event.locationCanberra, Australia
melbourne.accessrightsOpen Access


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