Variants of the Borda count method for combining ranked classifier hypotheses

Abstract

The Borda count is a simple yet effective method of combining rankings. In pattern recognition, classifiers are often able to return a ranked set of results. Several experiments have been conducted to test the ability of the Borda count and two variant methods to combine these ranked classifier results. By using artificial data, domain-specific results were avoided. The results show the strength of the Borda count when many errors occur in the results, but also show its weakness in case of a limited number of large ranking errors.