# Aggregation of Classifiers: A Justifiable Information Granularity Approach

arXiv: https://arxiv.org/abs/1703.05411 · HTML: https://mlsanity.com/paper/1703.05411/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity](https://mlsanity.com/paper/2405.06904/) (2024)
- [A unifying approach on bias and variance analysis for classification](https://mlsanity.com/paper/2101.01765/) (2021)
- [Combining Predictions under Uncertainty: The Case of Random Decision Trees](https://mlsanity.com/paper/2208.07403/) (2022)
- [GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure](https://mlsanity.com/paper/2506.02390/) (2025)

## Abstract (arXiv)

In this study, we introduce a new approach to combine multi-classifiers in an ensemble system. Instead of using numeric membership values encountered in fixed combining rules, we construct interval membership values associated with each class prediction at the level of meta-data of observation by using concepts of information granules. In the proposed method, uncertainty (diversity) of findings produced by the base classifiers is quantified by interval-based information granules. The discriminative decision model is generated by considering both the bounds and the length of the obtained intervals. We select ten and then fifteen learning algorithms to build a heterogeneous ensemble system and then conducted the experiment on a number of UCI datasets. The experimental results demonstrate that the proposed approach performs better than the benchmark algorithms including six fixed combining methods, one trainable combining method, AdaBoost, Bagging, and Random Subspace.