Conference Proceedings

An interval-based aggregation approach based on Bagging and Interval Agreement Approach in ensemble learning

M Maadi, U Aickelin, HA Khorshidi

2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 | IEEE | Published : 2020

Abstract

The 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 ag..

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