META-DES: A dynamic ensemble selection framework using meta-learning

• Published in: Pattern recognition 2015-05, Vol.48 (5), p.1925-1935
• Main Authors: Cruz, Rafael M.O., Sabourin, Robert, Cavalcanti, George D.C., Ing Ren, Tsang
• Format: Article
• Language: English
• Subjects: Classification; Classifier competence; Classifiers; Criteria; Dynamic ensemble selection; Dynamical systems; Dynamics; Ensemble of classifiers; Estimates; Meta-learning; Query processing; Training; Trains
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2014.12.003

Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy in local regions of the feature space around the query instance. However, using only one criterion about the behavior of a base classifier is not sufficient to accurately estimate its level of competence. In this paper, we present a novel dynamic ensemble selection framework using meta-learning. We propose five distinct sets of meta-features, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of input samples. The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance. During the generalization phase, the meta-features are extracted from the query instance and passed down as input to the meta-classifier. The meta-classifier estimates, whether a base classifier is competent enough to be added to the ensemble. Experiments are conducted over several small sample size classification problems, i.e., problems with a high degree of uncertainty due to the lack of training data. Experimental results show that the proposed meta-learning framework greatly improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques. •We propose a novel dynamic ensemble selection framework using meta-learning.•We present five sets of meta-features to measure the competence of a classifier.•Results demonstrate the proposed framework outperforms current techniques.