A Transfer Classification Method for Heterogeneous Data Based on Evidence Theory

It remains a challenging problem for data classification without training patterns. In many applications, there may exist some labeled data in other related domains (called source domain), and such labeled data can be helpful to solve the classification problem in the target domain. It is considered that the source domain and target domain are heterogeneous here and they represent the distinct feature spaces. A new transfer classification method for heterogeneous data is proposed based on the evidence theory. Some pattern pairs in the source domain and target domain are given to predict the link of these two domains. For each pattern in the target domain, we estimate its possible mapping value in the source domain using these pattern pairs with a self-organizing map (SOM) technique, and then the mapping value is classified using the labeled data in the source domain. However, the patterns with close values in the target domain may have more or less different values in the source domain due to the distinct characteristics of these two domains. So the mapping value can be very uncertain sometimes. In such a case, the target pattern is allowed to have multiple mapping values with different weights/reliabilities in the source domain. These mapping values can produce different classification results. The evidence theory is good at characterizing and combining uncertain information. In order to improve the classification accuracy, a new evidence-based weighted fusion method is developed for combining these classification results, which are discounted by the corresponding weights under the belief functions framework, and the final class decision is made according to the combination result. In experimental applications, some heterogeneous remote sensing data and UCI data are used to test the performance of new method with respect to several other methods, and it shows that the new method can efficiently improve the classification accuracy.