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The main aim in ensemble learning is using multiple individual classifiersoutputs rather than one classifier output to aggregate them for more accurateclassification. Generating an ensemble classifier generally is composed ofthree steps: selecting the base classifier, applying a sampling strategy togenerate different individual classifiers and aggregation the classifiersoutputs. This paper focuses on the classifiers outputs aggregation step andpresents a new interval-based aggregation modeling using bagging resamplingapproach and Interval Agreement Approach (IAA) in ensemble learning. IAA is aninteresting and practical aggregation approach in decision making which wasintroduced to combine decision makers opinions when they present their opinionsby intervals. In this paper, in addition to implementing a new aggregationapproach in ensemble learning, we designed some experiments to encourageresearchers to use interval modeling in ensemble learning because it preservesmore uncertainty and this leads to more accurate classification. For thispurpose, we compared the results of implementing the proposed method to themajority vote as the most common and successful aggregation function in theliterature on 10 medical data sets to show the better performance of theinterval modeling and the proposed interval-based aggregation function inbinary classification when it comes to ensemble learning. The results confirmthe good performance of our proposed approach.

A new interval-based aggregation approach based on bagging and Interval Agreement Approach (IAA) in ensemble learning