Affiliations: [a] Stony Brook University, Stony Brook, NY, USA | [b] Sharif University of Technology, Tehran, Iran | [c] Georgia Institute of Technology, Atlanta, GA, USA
Correspondence:
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Corresponding author: Mohammadzaman Zamani, Stony Brook University, Stony Brook, NY 11794, USA. E-mail:mzamani@cs.stonybrook.edu
Abstract: With the increasing volume of data, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The weighted majority and the randomized weighted majority (RWM) algorithms are two well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this problem by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.
