  The main aim in ensemble learning is using multiple individual classifiers
outputs rather than one classifier output to aggregate them for more accurate
classification. Generating an ensemble classifier generally is composed of
three steps: selecting the base classifier, applying a sampling strategy to
generate different individual classifiers and aggregation the classifiers
outputs. This paper focuses on the classifiers outputs aggregation step and
presents a new interval-based aggregation modeling using bagging resampling
approach and Interval Agreement Approach (IAA) in ensemble learning. IAA is an
interesting and practical aggregation approach in decision making which was
introduced to combine decision makers opinions when they present their opinions
by intervals. In this paper, in addition to implementing a new aggregation
approach in ensemble learning, we designed some experiments to encourage
researchers to use interval modeling in ensemble learning because it preserves
more uncertainty and this leads to more accurate classification. For this
purpose, we compared the results of implementing the proposed method to the
majority vote as the most common and successful aggregation function in the
literature on 10 medical data sets to show the better performance of the
interval modeling and the proposed interval-based aggregation function in
binary classification when it comes to ensemble learning. The results confirm
the good performance of our proposed approach.
