Why does output normalization create problems in multiple classifier systems?

A combination of classifiers is a promising direction for obtaining better classification systems. However the outputs of different classifiers may have different scales and hence the classifier outputs are incomparable. Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to avoid this problem, the measurement level classifier outputs are generally normalized. However recent studies have proven that output normalization may provide some problems. For instance, the multiple classifier system's performance may become worse than that of a single individual classifier. This paper presents some interesting observations about the reason why such undesirable behavior occurs.