Information Market-Based Decision Fusion
Management Science, 55(5), 827-842, 2009
Posted: 24 Jan 2013 Last revised: 28 Jan 2013
Date Written: August 19, 2008
Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms committing financial frauds. Multi-classifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper we present Information Market based Fusion (IMF), a novel multi-classifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market based approach, IMF can adjust to changes in base-classifier performance, while not requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, Majority, Average and Weighted Average; for high positive ratios, IMF outperforms Majority, while performing on par with Average and Weighted Average. When the true classes of all objects are revealed, IMF outperforms Weighted Average and Majority, and at marginal level of significance, outperforms Average.
Keywords: multi-classifier combination, decision fusion, information markets, software agent
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