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

TL;DR

This paper introduces a novel interval-based aggregation method using bagging and Interval Agreement Approach (IAA) to improve ensemble learning accuracy by better handling uncertainty in classifier outputs.

Contribution

It presents a new interval-based aggregation model combining bagging and IAA, demonstrating improved performance over traditional majority voting in ensemble classification.

Findings

Proposed method outperforms majority vote in accuracy

Interval modeling preserves more uncertainty

Effective on medical datasets

Abstract

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.