Ekşioğlu, KubilayQureshi, Muhammad AnjumTekin, Cem2018-04-122018-04-122017http://hdl.handle.net/11693/37587Date of Conference: 15-18 May 2017Conference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017In this paper, a novel online scheme for classification, which is based on the contextual-variant of Weighted Average Forecaster Algorithm is proposed. The proposed method adaptively partitions the data space based on contexts, and tradeoffs exploration and exploitation when fusing the predictions of the experts. The proposed algorithm is verified on disease data available in UCI Online Machine Learning Repository. These results prove the robustness, effectiveness and versatility in terms of performance and low computational cost of the proposed system in the field of medical diagnostics.TurkishBiased datasetsContextual banditsDecision treesExponential weightsOnline classificationConference Paper10.1109/SIU.2017.7960578