Classifier combining through trimmed means and order statistics

Combining the outputs of multiple neural networks has led to substantial improvements in several difficult pattern recognition problems. We introduce and investigate robust combiners, a family of classifiers based on order statistics. We focus our study on the analysis of the decision boundaries, and how these boundaries are affected by order statistics combiners. In particular, we show that using the ith order statistic, or a linear combination of the ordered classifier outputs is quite beneficial in the presence of outliers or uneven classifier performance. Experimental results on several public domain data sets corroborate these findings