  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.
