Extrapolating Expected Accuracies for Large Multi-Class Problems

Charles Zheng; Rakesh Achanta; Yuval Benjamini

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arXiv · 1712.09713

Extrapolating Expected Accuracies for Large Multi-Class Problems

Charles Zheng·Rakesh Achanta·Yuval Benjamini

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Abstract

The difficulty of multi-class classification generally increases with the number of classes. Using data from a subset of the classes, can we predict how well a classifier will scale with an increased number of classes? Under the assumptions that the classes are sampled identically and independently from a population, and that the classifier is based on independently learned scoring functions, we show that the expected accuracy when the classifier is trained on k classes is the (k-1)st moment of a certain distribution that can be estimated from data. We present an unbiased estimation method based on the theory, and demonstrate its application on a facial recognition example.

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BibTeX RIS

Charles Zheng, Rakesh Achanta, Yuval Benjamini. 2017-12-27. Extrapolating Expected Accuracies for Large Multi-Class Problems. https://arxiv.org/abs/1712.09713

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