DYNAMIC ENSEMBLE SELECTION WITH REGIONAL EXPERTISE

Many recent works have shown that ensemble methods yield better generalizability over single classifier approach by aggregating the decisions of all base learners in machine learning tasks. To address the redundancy and inaccuracy issues with the base learners in ensem-ble methods, classifier/ensemble selection methods have been proposed to select one single classifier or an ensemble (a subset of all base learners) to classify a query pattern. This final classifier or ensemble is determined either statically before prediction or dynamically for every query pattern during prediction. Static selection approaches select classifier and ensemble by evaluating classifiers in terms of accuracy and diversity. While dynamic classi-fier/ensemble selection (DCS, DES) methods incorporate local information for a dedicated classifier/ensemble to each query pattern. Our work focuses on DES by proposing a new DES framework DES with Regional Expertise (DES-RE). The success of a DES system lies in two factors: the quality of base learners and the optimality of ensemble selection. DES-RE proposed in our work addresses these two chal-lenges respectively. 1) Local expertise enhancement. A novel data sampling and weightin