Online local pool generation for dynamic classifier selection

Dynamic Classifier Selection (DCS) techniques have difficulty in selecting the most competent classifier in a pool, even when its presence is assured. Since the DCS techniques rely only on local data to estimate a classifiers competence, the manner in which the pool is generated could affect the choice of the best classifier for a given instance. That is, the global perspective in which pools are generated may not help the DCS techniques in selecting a competent classifier for instances that are likely to be misclassified. Thus, it is proposed in this work an online pool generation method that produces a locally accurate pool for test samples in difficult regions of the feature space. The difficulty of a given area is determined by the estimated classification difficulty of the instances in it. That way, by using classifiers that were generated in a local scope, it could be easier for the DCS techniques to select the best one for those instances they would most probably misclassify. For the query samples surrounded by easy instances, a simple nearest neighbors rule is used in the proposed method. In order to identify in which cases the local pool is used in the proposed scheme, an analysis on the correlation between instance hardness and DCS techniques is performed in this work, and it is proposed the use of an instance hardness measure that conveys the degree of local class overlap near a given sample. Experimental results show that the DCS techniques were more able to select the most competent classifier for difficult instances when using the proposed local pool than when using a globally generated pool. Moreover, the proposed technique yielded significantly greater recognition rates in comparison to a Bagging-generated pool and two other global generation schemes for all DCS techniques evaluated. The performance of the proposed technique was also significantly superior to three state-of-the-art classification models and was statistically equivalent to five of them. (C) 2018 Elsevier Ltd. All rights reserved.