Uncertain Pattern Classification Based on Evidence Fusion in Different Domains

It is a challenging problem for pattern classification with few labeled instances. Transfer learning provides an efficient solution to improve the classification accuracy using some training knowledge in the related domain (called source domain). Nevertheless, the single transformation in one direction may be uncertain in some cases, and this is harmful for classification. So we propose a new classification method based on the fusion of data transformations in different directions between source domain and target domain. At first, the mapping of target in the source domain is estimated by K-nearest neighbor technique using some one-to-one instance pairs, and the estimated mapping instance (pattern) can be classified in the source domain according to the available training data. Then, the credibility of classification result is evaluated. If the credibility achieves the expected threshold, the classification result is directly output. Otherwise, it indicates that the transformation may be not very reliable, and the labeled instances in source domain will be transferred to target domain for the classification of target. The two versions of classification results will be fused with different weights based on evidential reasoning, and the weighting factors are optimized using the available training instances. By doing this, we can efficiently reduce the uncertainty of transformation and improve the classification accuracy. Some real data sets from UCI have been employed to validate the effectiveness of the proposed by comparing with other related methods.