In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learned from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning and decision-making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.

The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms in pattern recognition field because it is very simple to understand but works quite well in practice. However, the performance of the kNN rule depends critically on its being given a good distance metric over the input space, especially in small data set situations. In this paper, a new kNN-based classifier, called BPkNN, is developed based on pairwise distance metrics and belief function theory. The idea of the proposal is that instead of learning a global distance metric, we first decompose it into learning a group of pairwise distance metrics. Then, based on each learned pairwise distance metric, a pairwise kNN (PkNN) sub-classifier can be adaptively designed to separate two classes. Finally, a polychotomous classification problem is solved by combining the outputs of these PkNN sub-classifiers in belief function framework. The BPkNN classifier improves the classification performance thanks to the new distance metrics which provide more flexibility to design the feature weights and the belief function-based combination method which can better address the uncertainty involved in the outputs of the sub-classifiers. Experimental results based on synthetic and real data sets show that the proposed BPkNN can achieve better classification accuracy in comparison with some state-of-the-art methods.

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image interpretation, which has made tremendous progress with the advancement of artificial intelligence technology. However, SAR imaging is sensitive to the operating conditions of platforms, resulting in large distribution discrepancy for the data collected on different platforms. Moreover, SAR target images are difficult to annotate due to the blurry textures, resulting in insufficient labeled data to train a model. Therefore, subject to the data distribution discrepancy and insufficient labeled data, SAR target recognition becomes a highly challenging task. Transfer adaptive learning (TAL) is a learning paradigm aimed at completing target tasks by transferring knowledge from relevant source domains, which is a promising technique for solving the aforementioned problems in SAR target recognition. However, there is currently no comprehensive survey about the application of transfer adaptation learning in SAR target recognition. To this end, we comprehensively summarized the development of transfer adaptive learning in SAR target recognition, and provided systematic guidance for future research. In our paper, we first summarized the electromagnetic features and visual features of SAR images used for target recognition, which can be potentially used for knowledge transfer. Then, we systematically reviewed the related literature according to the homogeneity of the transfer domains, the modality of the data in the source domain, and the category of the TAL methods. The available datasets that can be used to validate the TAL methods for SAR target recognition were also summarized for the researcher's convenience. We also conducted comparative experiments on these data to demonstrate the performance of TAL methods. Finally, we analyzed the main challenges of the current methods and pointed out several directions worth studying in the future.