Contribution of data complexity features on dynamic classifier selection

Different dynamic classifier selection techniques have been proposed in the literature to determine among diverse classifiers available in a pool which should be used to classify a test instance. The individual competence of each classifier in the pool is usually evaluated taking into account its accuracy on the neighborhood of the test instance in a validation dataset. In this work we investigate the possible contribution of considering during the classifier evaluation the use of features related to the problem complexity. Since usually the pool generation technique does not assure diversity, the idea is to consider diversity during the selection. Basically, we select a classifier trained in subset of data showing similar complexity than that observed in neighborhood of the test instance. We expect that this similarity in terms of complexity allow us to select a more competent classifier. Experiments on 30 classification problems representing different levels of difficulty have shown that the proposed selection method is comparable to well known dynamic selection strategies. When compared with other DS approaches it was able to win on 123 over 150 experiments. This promising results indicate that further investigation must be done to increase diversity in terms of data complexity during the process of pool generation.