Abstract:
Dynamic Classifier Selection (DCS) techniques classifies the test sample 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. We have suggested 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 proposed in this paper altogether enhance performance and
accuracy of the framework in contrast with other dynamic selection techniques in literature.
