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This paper introduces a new perspective on multi-class ensembleclassification that considers training an ensemble as a state estimationproblem. The new perspective considers the final ensemble classifier model as astatic state, which can be estimated using a Kalman filter that combines noisyestimates made by individual classifier models. A new algorithm based on thisperspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is alsopresented in this paper which shows the practical applicability of the newperspective. Experiments performed on 30 datasets compare KFHE withstate-of-the-art multi-class ensemble classification algorithms and show thepotential and effectiveness of the new perspective and algorithm. Existingensemble approaches trade off classification accuracy against robustness toclass label noise, but KFHE is shown to be significantly better or at least asgood as the state-of-the-art algorithms for datasets both with and withoutclass label noise.

Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters