Proceedings of the 9th IAPR International Workshop on
Document Analysis Systems, ACM, Boston (2010), pp.
Oleg Golubitsky, Stephen M. Watt
We consider the problem of dealing with irrelevant votes when a multi-case
classifier is built from an ensemble of binary classifiers. We show how run-off
elections can be used to limit the effects of irrelevant votes and the occasional
errors of binary classifiers, improving classification accuracy. We consider as a
concrete classification problem the recognition of handwritten mathematical
characters. A succinct representation of handwritten symbol curves can be obtained
by computing truncated Legendre-Sobolev expansions of the coordinate functions.
With this representation, symbol classes are well linearly separable in low
dimension which yields fast classification algorithms based on linear support
vector machines. A set of 280 different symbols was considered, which gave 1635
classes when different variants are labelled separately. With this number of
classes, however, the effect of irrelevant classifiers becomes significant, often
causing the correct class to be ranked lower. We introduce a general technique to
correct this effect by replacing the conventional majority voting scheme with a
runoff election scheme. We have found that such runoff elections further cut the
top-1 mis-classification rate by about half.