Noise-Resilient Ensemble Learning using Evidence Accumulation Clustering

TL;DR

This paper introduces a noise-resilient ensemble classification method inspired by Evidence Accumulation Clustering, which improves accuracy and robustness against network corruption and can combine diverse classifiers.

Contribution

It proposes a novel ensemble method that enhances resilience to noisy or corrupted network conditions by adapting evidence accumulation clustering for classification tasks.

Findings

Outperforms naive voting under high noise levels

Shows increased robustness in multi-class datasets

Flexible in combining classifiers with different label definitions

Abstract

Ensemble Learning methods combine multiple algorithms performing the same task to build a group with superior quality. These systems are well adapted to the distributed setup, where each peer or machine of the network hosts one algorithm and communicate its results to its peers. Ensemble learning methods are naturally resilient to the absence of several peers thanks to the ensemble redundancy. However, the network can be corrupted, altering the prediction accuracy of a peer, which has a deleterious effect on the ensemble quality. In this paper, we propose a noise-resilient ensemble classification method, which helps to improve accuracy and correct random errors. The approach is inspired by Evidence Accumulation Clustering , adapted to classification ensembles. We compared it to the naive voter model over four multi-class datasets. Our model showed a greater resilience, allowing us to recover prediction under a very high noise level. In addition as the method is based on the evidence accumulation clustering, our method is highly flexible as it can combines classifiers with different label definitions.