Pattern classification based on the combination of the selected sources of evidence

In the complex pattern classification problem, the fusion of multiple classification results produced by different attributes is able to efficiently improve the accuracy. Evidence theory is good at representing and combining the uncertain information, and it is employed here. Each attribute (set) can be considered as one source of evidence (information). In some applications, the observation of target attributes can be costly, and some unreliable information sources may harm the fusion result. Therefore, we want to use as few as possible sources of information with high quality to achieve the admissible classification accuracy. So we propose a new fusion method based on the adaptive selection of the information sources for pattern classification. For each pattern, the attribute (set) producing the highest accuracy among the various ones will be chosen to classify the pattern at first. If the reliability of classification result, which is evaluated by the K-nearest neighbors (K-NN) technique using training data, cannot satisfy the request, the next attribute source will be chosen according to its classification performance on the selected neighborhoods of the object. In the fusion, the classification results corresponding to different attributes are assigned different weights because of their different classification abilities, and the weighted evidence combination method is adopted to produce the best possible classification performance. Several real data sets from UCI have been used for the evaluation of the proposed method by comparison with other related fusion methods, and it shows that our new method can produce higher accuracy with smaller number of information sources than the other fusion methods which are directly used to combine all the sources of information.