Noise-Resilient Ensemble Learning using Evidence Accumulation Clustering
This addresses the issue of degraded ensemble quality due to network corruption in distributed systems, though it appears incremental as it adapts an existing clustering technique to classification.
The paper tackles the problem of noise-corrupted predictions in distributed ensemble learning by proposing a noise-resilient classification method based on Evidence Accumulation Clustering, which showed greater resilience and allowed recovery of predictions under very high noise levels in comparisons over four multi-class datasets.
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.