The impact of the diversity on multiple classifier system performance: Identifying changes in the amount of fuel in the fleet management system

When it comes to the use of any recognition systems in the real world environment, it turns out that the reality differs from the theory. There is an assumption that the distribution of the incoming data will be at least similar to the distribution of the data, which were used during the learning process and that learning dataset represents the entire space of the problem. In fact, the incoming data differ from the training set and usually cover only a part of the feature space. Very often we have to deal with imbalanced datasets which leads to underfitting of classifiers in the final ensemble. In this paper we present the Multiple Classifier System based on Random Reference Classifier in the problem of fuel level change detection in the fleet management systems. The ensemble selection process uses probabilistic measures of competence and diversity at the same time. We compare different methods to determine the diversity within the ensemble.