Estimating the Bayes error rate through classifier combining
Kagan Tumer, Joydeep Ghosh · 1996
The Bayes error provides the lowest achievable error rate for a given pattern classification problem. There are several classical approaches for estimating or finding bounds for the Bayes error. One type of approach focuses on obtaining analytical bounds, which are both difficult to calculate and dependent on distribution parameters that may not be known. Another strategy is to estimate the class densities through non-parametric methods, and use these estimates to obtain bounds on the Bayes error. This article presents a novel approach to estimating the Bayes error based on classifier combining techniques. For an artificial data set where the Bayes error is known, the combiner-based estimate outperforms the classical methods.