A Dynamic Ensemble Selection Framework Using Dynamic Weighting Approach

Abstract

In Dynamic Classifier Selection (DCS) techniques, test sample is classified only by the most competent classifiers. Hence, the major problem in DCS is to find the measures by which competence of classifiers in a pool can be calculated to find out the most competent classifiers. To tackle these issues, we suggest a Framework for Dynamic Ensemble Selection (DES) that uses more than one criterion to calculate the base classifier’s competence level. The framework has three major steps. In first step, training data is used to create a pool consisting of different classifiers. In second step meta-classifier training is performed by extracting meta-features from training data. In third step meta-classifier uses meta-features extracted from test sample to perform an ensemble selection and to predict the final output. In this paper, we suggest some improvements in second step (training) and last step (generalization) of the framework. In training phase, four different models are used as meta-classifiers. While in generalization phase, dynamic weighting scheme is used where meta-classifiers will dynamically assign weights to selected competent classifiers based on their competence level and final decision will be aggregated using a weighting voting scheme. The modifications purposed in this paper altogether enhance performance and accuracy of the framework in contrast with other dynamic selection techniques in literature.