Dynamic Classifier Selection: Recent Advances and Perspectives
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Multiple Classifier Systems (MCS) have been widely studied as an alternative for increasing accuracy in pattern recognition. One of the most promising MCS approaches is Dynamic Selection (DS), in which the base classifiers are selected on the fly, according to each new sample to be classified. DS has become an active research topic in the multiple classifier systems literature in past years. This has been due to the fact that more and more works are reporting the superior performance of such techniques over static combination approaches, especially when dealing with small sized datasets, imbalanced problems and noise distributions.
DS techniques work by estimating the competence level of each classifier from a pool of classifiers. Only the most competent or an ensemble containing the most competent classifiers is selected to predict the label of a specific test sample. The rationale for such techniques is that not every classifier in the pool is an expert in classifying all unknown samples; rather, each base classifier is an expert in a different local region of the feature space. So, the key aspect in dynamic selection techniques is how to estimate the competence of the base classifiers in the local regions and select the most competent ones for the classification of any given query samples.
The goal of this tutorial is to present the attendees a detailed formulation of each step involved in a dynamic selection system, from the initial steps of generating a pool of classifiers, to the final steps on how the base classifiers are selected based on each new query sample. Moreover, experimental evaluation of the state-of-the-art dynamic selection techniques as well as guidelines for future research is also presented.