Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classifiers with Multiple Adaptive Mechanisms
作者： Rashid Bakirov , Bogdan Gabrys
DOI: 10.1007/978-3-642-41142-7_65
关键词:
摘要: We consider online classification problem, where concepts may change over time. A prominent model for creation of dynamically changing ensemble is used in Dynamic Weighted Majority (DWM) method. analyse this model, and address its high sensitivity to misclassifications resulting unnecessary large ensembles, particularly while running on noisy data. propose evaluate various criteria adding new experts an ensemble. test our algorithms a comprehensive selection synthetic data establish that they lead the significant reduction number created show slightly better accuracy rates than original models non-ensemble adaptive benchmarking.
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