A Theoretical Study on Six Classifier Fusion Strategies

Authors:
Ludmila I. Kuncheva
Affiliations:
Univ. of Wales, UK
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2002

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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.