Incremental Classifier Fusion and its Application in Industrial Monitoring and Diagnostics
Sprache des Titels:
Learning in Non-Stationary Environments: Methods and Applications
Pattern recognition techniques have shown their usefulness for monitoring
and diagnosing many industrial applications. The increasing production rates and the growing databases generated by these applications require learning techniques that can adapt their models incrementally, without revisiting previously used data. Ensembles of classifiers have been shown to improve the predictive accuracy as well as the robustness of classification systems. In this work several well-known classi-
fier fusion methods (Fuzzy Integral, Decision Templates, Dempster-Shafer Combination and Discounted Dempster-Shafer Combination) are extended to allow incremental adaptation. Additionally, an incremental classifier fusion method using an evolving clustering approach is introduced?named Incremental Direct Cluster-based ensemble. A framework for strict incremental learning is proposed in which the ensemble and its member classifiers are adapted concurrently. The proposed incremental classifier fusion methods are evaluated within this framework for two industrial applications: on-line visual quality inspection of CD imprints and prediction of maintenance actions for copiers from a large historical database.