Combining independent and unbiased classifiers using weighted average

In a classification problem, improved accuracy can be obtained in many situations by using the combination of several classifiers instead of a single one. Turner and Gosh (1999) derived the error reduction that can be obtained by combining unbiased classifiers with independent errors using a simple average. We present an extension of this result by finding the improvement obtained when combining classifiers using weighted average. We also prove that for unbiased classifiers with independent errors the best combination of N classifiers corresponds to a weighted average, where the combination coefficient of each classifier is equal to 1/N. This means that in these cases the simple average should be used. We present experiments illustrating our results