Multi-represented Classification Based on Confidence Estimation

Complex objects are often described by multiple representations modeling various aspects and using various feature transformations. To integrate all information into classification, the common way is to train a classifier on each representation and combine the results based on the local class probabilities. In this paper, we derive so-called confidence estimates for each of the classifiers reflecting the correctness of the local class prediction and use the prediction having the maximum confidence value. The confidence estimates are based on the distance to the class border and can be derived for various types of classifiers like support vector machines, k-nearest neighbor classifiers, Bayes classifiers, and decision trees. In our experimental results, we report encouraging results demonstrating a performance advantage of our new multi-represented classifier compared to standard methods based on confidence vectors.