Abstract
This paper proposes a voting strategy for knowledge integration and decision making systems with information uncertainty. As ensemble learning methods have recently attracted growing attention from both academia and industry, it is critical to understand the fundamental problem of voting strategy for such learning methodologies. Motivated by the signal to noise ratio (SNR) concept, we propose a method that can vote optimally according to the knowledge level of each hypothesis. The mathematical framework based on gradient analysis is used to find the optimal weights, and a voting algorithm, BoostVote, is presented in detail in this paper. Simulation analyses based on synthetic data and real-world data sets with comparison to the existing voting rules demonstrate the effectiveness of this method.
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He, H., Cao, Y., Wen, J., Cheng, S. (2008). A Boost Voting Strategy for Knowledge Integration and Decision Making. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_53
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DOI: https://doi.org/10.1007/978-3-540-87732-5_53
Publisher Name: Springer, Berlin, Heidelberg
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