A Novel Simulated Annealing Based Training Algorithm for Data Stream Processing Ensemble Classifier.

Training of compound ensemble classifier systems might be computationally complex and hence time consuming task. Not only elementary classifiers are to be trained, but also model of the ensemble has to be updated. Therefore, an efficiency of the training shall be considered as a compound quality which consists of not only a classification accuracy but also a running time. This gains a special importance while dealing with data streams where data arrive at high pace and the system update shall be done promptly. In this paper we present an application of Simulated Annealing based algorithm for training of data stream processing ensemble. The evaluation of our method is performed in series of experiments which show that our ensemble perform very effectively in term of accuracy and processing time.