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Belief-based system for fusing multiple classification results with local weights

Abstract. The fusion of multiple classifiers is an effective way to improve classification performance. Classifiers trained on different datasets generally have different qualities on classification. Moreover, the classification results of different objects (patterns) by a common classifier may also show different reliabilities. So, we propose a system for fusing multiple classification results with local weights based on belief functions theory. For one classifier, the training dataset is divided into some clusters and each cluster is used to train a weight to represent the reliability of this classifier used for classifying objects in this cluster. Thus, each classifier has multiple different weights corresponding to the patterns in different clusters. The weights can be optimized by minimizing the sum of distances between the weighted fusion results and the truths in all clusters. For each classifier, the object to classify is first assigned to the closest cluster according to its attributes, and then its classification result will be discounted with the corresponding weight. Multiple discounted results are combined using Dempster’s rule. To reduce the errors, a soft decision-making rule is developed by modeling the partial imprecision. If a hard decision shows a high risk of error, this object will be committed to a set of possible classes. Such imprecision that can be clarified using other techniques is usually considered better than an error. So classification efficiency of imprecise decision is defined to be lower than that of a correct result but higher than that of an error. For the object to classify, the final decision is made via comparing the efficiency of hard decisions with that of imprecise decisions using patterns in the closest cluster. Finally, some real datasets are used in experimental applications to demonstrate the effectiveness of the proposed method by comparison with other related fusion methods.