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A Theoretical Study on Six Classifier Fusion Strategies
February 2002 (vol. 24 no. 2)
pp. 281-286

Abstract—We look at a single point in the feature space, two classes, and $L$ classifiers estimating the posterior probability for class $\omega_1$. Assuming that the estimates are independent and identically distributed (normal or uniform), we give formulas for the classification error for the following fusion methods: average, minimum, maximum, median, majority vote, and oracle.

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Index Terms:
Classifier combination, theoretical error, fusion methods, order statistics, majority vote, independent classifiers.
Ludmila I. Kuncheva, "A Theoretical Study on Six Classifier Fusion Strategies," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 2, pp. 281-286, Feb. 2002, doi:10.1109/34.982906
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