Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
This addresses the problem of improving classification accuracy and robustness to noise in ensemble methods for machine learning practitioners, though it appears incremental as it builds on existing ensemble and Kalman filter concepts.
The paper tackled multi-class ensemble classification by framing it as a state estimation problem using Kalman filters, resulting in the KFHE algorithm that performed significantly better or as good as state-of-the-art methods on 30 datasets, including those with class label noise.
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by individual classifier models. A new algorithm based on this perspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is also presented in this paper which shows the practical applicability of the new perspective. Experiments performed on 30 datasets compare KFHE with state-of-the-art multi-class ensemble classification algorithms and show the potential and effectiveness of the new perspective and algorithm. Existing ensemble approaches trade off classification accuracy against robustness to class label noise, but KFHE is shown to be significantly better or at least as good as the state-of-the-art algorithms for datasets both with and without class label noise.