Dynamic ensemble selection for data streams and multi-view learning

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Multiple Classifier System (MCS) is an active area of research in machine learning and pattern recognition. Several studies have been published demonstrating its advantages over individual classifier models either from the theoretical or empirical points of view. One of the most promising MCS approaches is Dynamic Ensemble Selection (DES), in which the base classifiers are selected on the fly, according to each new sample to be classified. DES has become an active research topic in the multiple classifier systems literature in past years due to recent works reporting dynamic ensembles' superior performance over static ones and monolithic classifiers. Despite the recent advancement in DES methods, there are still vastly unexplored areas by the DES community. Firstly, most of the conducted research considers a stationary environment. However, the majority of real-world applications have a non-static environment where data usually comes in the form of data streams and is continuously changing. Handling non-static environments pose new challenges for DES methods since they need to learn incrementally and cope with multiple problems found in data streams such as the addition of new classes, new features, and new views of the data. Secondly, current research on DES techniques considers a single view of the problem (i.e., a single feature space). However, often a single-view data cannot properly describe all examples in the data, and a multi-view approach is therefore required to improve generalization performance. Several real-world applications, such as text classification, biometrics, and the Internet of things (IoT), greatly benefit from adopting a multi-view learning approach. Hence, this research program's main objective is to propose DES methodologies to deal with large data streams and multi-view learning. This objective will be handled through three steps: (i) Development of a pool generation approach specially crafted for dynamic ensemble selection techniques; (ii) Adaptation of dynamic ensemble selection for dealing with large volumes of data; and (iii) Development of a dynamic multi-view ensemble selection methodology. This research program will lead to robust dynamic ensemble models that will benefit applications where data comes inherently through streams such as financial data classification, fake news detection, and traffic control. Moreover, I expect DES methods to significantly impact the field of multi-view learning as this breakthrough methodology will allow us to efficiently solve fundamental problems in this field by selecting the most relevant views of the data on-the-fly.