# A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection

arXiv: https://arxiv.org/abs/2012.02481 · HTML: https://mlsanity.com/paper/2012.02481/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition](https://mlsanity.com/paper/2110.07191/) (2021)
- [Vibration-Based Condition Monitoring By Ensemble Deep Learning](https://mlsanity.com/paper/2110.06601/) (2021)
- [Proximity-Based Evidence Retrieval for Uncertainty-Aware Neural Networks](https://mlsanity.com/paper/2509.13338/) (2025)

## Abstract (arXiv)

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this paper, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster-Shafer theory (DST). However, in cases with conflicting evidences, the DST may give counter-intuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it is applied for classifying polycrystalline Nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that that the proposed method improves the classification accuracy and outperforms the individual classifiers.