Uncertain data classification based on the fusion of local and global information

In the complex pattern classification problem, the reliability of classifier output for the patterns located at different regions of the data set may be different. In order to efficiently improve the classification accuracy, we propose a new method to correct the original classifier output using the local knowledge of the classifier performance in different regions. The training data set can be divided into some small clusters corresponding to different regions. The prior knowledge of the classifier performance on each cluster is characterized by a confusion matrix representing the conditional probability of the pattern belonging to one class but committed to another class by the classifier. The matrix associated with each cluster is learnt by minimizing an error criteria using training data, which is assigned different weights to achieve the highest possible accuracy. If the classification accuracy of the training data in one cluster can be improved according to the corrected classification results, the associated confusion matrix becomes valid. Otherwise, the confusion matrix is invalid and patterns in this cluster cannot be modified any more. For each object, if it lies in the cluster with valid confusion matrix, its classification result will be corrected by the matrix before making the class decision. The above correction process can be regarded as the fusion of local and global information. Several experiments are given to test the performance of the proposed method using real data sets, and it shows that the new method is able to efficiently improve the classification accuracy compared with other related methods.