Issue No. 02 - February (2002 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.982906
<p><b>Abstract</b>—We look at a single point in the feature space, two classes, and <tmath>$L$</tmath> classifiers estimating the posterior probability for class <tmath>$\omega_1$</tmath>. 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.</p>
Classifier combination, theoretical error, fusion methods, order statistics, majority vote, independent classifiers.
L. I. Kuncheva, "A Theoretical Study on Six Classifier Fusion Strategies," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 281-286, 2002.