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

Abstract

The main aim in ensemble learning is using multiple 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 presenting a new aggregation approach in ensemble learning, we design 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 commonly used and successful aggregation function in the literature, for 10 medical data sets. The results show the better performance of the interval modeling and 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.