Comparison Between Product and Mean Classifier Combination Rules
Dmj Tax, Rpw Duin, M. van Breukelen · 1997
To obtain better classification results, the outputs of an ensemble of classifiers can be combined instead of just choosing the best classifier. This combining is often done by using a simple linear combination of the outputs of the classifiers or by using order statistics (using the order in the outputs for different classes). In this paper we will show that using the normalized product of the outputs of the classifiers can be more powerful for classification performance. We will show in which cases a product combination is to be preferred and where a combination by averaging can be more useful. This will be supported by theoretical and experimental observations. 1 Introduction Certainly a very important property for a classifier is to respond meaningfully to novel patterns, i.e. the classifier generalizes [Wol94]. To obtain a network which generalizes well, one often constructs several different classifiers. Each of these classifiers have different decision boundaries and generalize...