Partial label learning (PLL) is a paradigm in weakly supervised learning. The goal is to identify the ground-truth label from a set of candidate labels associated with a given sample. However, due to the ambiguity of labels, improving the accuracy of ground-truth label recognition is a challenge. In this paper, we propose an innovative training framework PLMR, for solving the PLL problem. Given the specificity of the PLL problem, its data is rich in valid information but significantly noisy. To overcome the problem, PLMR incorporates mutual information (MI) theory to mine the potential information of candidate labels and data features to distinguish between positive and negative sample pairs. This operation is to effectively utilize the raw data information in PLL and reduce the class conflict problem. In this way, the discriminative power of representation is improved according to the positive and negative pair selection strategy. At the same time, PLMR introduces cluster centers to optimize subsequent tasks, combining data augmentation samples with the K-means cross-attention mechanism to refine the optimized cluster centers. This is to improve the ability of the clustering centre to accurately represent the class information, thus improving the overall quality of the clusters. Further, through the ambiguity marking correction mechanism, weights are calculated based on the association between cluster centers and sample representations to guide model training. Experimental results show that PLMR demonstrates excellent classification performance on multiple datasets, verifying its effectiveness and sophistication.

Missing attribute values frequently affect data classification, reducing accuracy as most models rely on complete datasets. Imputing missing values is typically used to restore data completeness, which is essential for building models. The effectiveness of imputation significantly impacts the classification accuracy. Therefore, improving imputed values’ quality is crucial for better classification outcomes. Here, we introduce a new distribution alignment framework (DAF) to address classification issues with complete training data but incomplete test data. Initially, DAF imputes missing test data values using mean vectors from complete training data, minimizing the first-order distributional discrepancies. Next, it aligns the second-order statistical distributions, specifically covariance matrices, of both training and imputed test data to derive a feature transformation matrix. This matrix generates new feature representations for the incomplete test data. The classifier trained on the complete training data then classifies the imputed test data under this new feature representation. The experiments on several benchmark datasets show that DAF usually outperforms many advanced methods, achieving the higher classification performance.

In pattern recognition, where the labeled data is scarce, transfer learning (also called domain adaptation in some cases) methods frequently come into play to transfer knowledge from the source domains to bolster the construction of classification models within the target domain. The judicious fusion of information from multiple source domains typically enhances classification precision. In light of this, we introduce a new Multi-source Distributed Transfer Learning (MDTL) framework designed to adeptly integrate complementary information across various source domains through the application of belief functions. In this approach, the distributions of each source and target domain are aligned independently. Subsequently, the resultant soft classification outcomes, facilitated by different source domains, are amalgamated using belief functions. This integration incorporates novel weighting factors that consider both distribution discrepancies and classifier effectiveness. The effectiveness of MDTL was assessed against a range of related methods, and the experimental findings confirm that it markedly improves classification accuracy in the target domain.

With the rapid development of Internet technology, social networks have become essential platforms for communication, and in the process, they generate vast amounts of user data that reflect interests, relationships, and habits. User role classification in social networks is critical for personalized recommendations, precision marketing, and security. In this work, we investigate Graph Neural Networks (GNNs) for user role classification in social networks, and a novel GNN architecture, termed Graph Topology-aware Refinement Network (GTRNet), is designed. GTRNet comprises two modules: Graph Topology Encoding (GTE) and Node Representation Refinement (NRR). They combine the node features and network topological information via convolutional layers to enhance the use role classification performance. The experimental results demonstrate that GTRNet usually outperforms the state-of-the-art methods on the Facebook, Cora, and Citeseer datasets, with micro-F1 score improvements of 0.021, 0.009, and 0.014, respectively. It verifies GTRNet’s effectiveness in addressing social network user role classification task.

Partial label learning (PLL) studies the problem of learning instance classification with a set of candidate labels and only one is correct. While recent works have demonstrated that the Vision Transformer (ViT) has achieved good results when training from clean data, its applications to PLL remain limited and challenging. To address this issue, we rethink the relationship between instances and object queries to propose K-means cross-attention transformer for PLL (KMT-PLL), which can continuously learn cluster centers and be used for downstream disambiguation tasks. More specifically, K-means cross-attention as a clustering process can effectively learn the cluster centers to represent label classes. The purpose of this operation is to make the similarity between instances and labels measurable, which can effectively detect noise labels. Furthermore, we propose a new corrected cross entropy formulation, which can assign weights to candidate labels according to the instance-to-label relevance to guide the training of the instance classifier. As the training goes on, the ground-truth label is progressively identified, and the refined labels and cluster centers in turn help to improve the classifier. Simulation results demonstrate the advantage of the KMT-PLL and its suitability for PLL.

For incomplete data classification, missing attribute values are often estimated by imputation methods before building classifiers. The estimated attribute values are not actual attribute values. Thus, the distributions of data will be changed after imputing, and this phenomenon often results in degradation of classification performance. Here, we propose a new framework called integration of multikinds imputation with covariance adaptation (MICA) based on evidence theory (ET) to effectively deal with the classification problem with incomplete training data and complete test data. In MICA, we first employ different kinds of imputation methods to obtain multiple imputed training datasets. In general, the distributions of each imputed training dataset and test dataset will be different. A covariance adaptation module (CAM) is then developed to reduce the distribution difference of each imputed training dataset and test dataset. Then, multiple classifiers can be learned on the multiple imputed training datasets, and they are complementary to each other. For a test pattern, we can combine the multiple pieces of soft classification results yielded by these classifiers based on ET to obtain better classification performance. However, the reliabilities/weights of different imputed training datasets are usually different, so the soft classification results cannot be treated equally during fusion. We propose to use covariance difference across datasets and accuracy of imputed training data to estimate the weights. Finally, the soft classification results discounted by the estimated weights are combined by ET to make the final class decision. MICA was compared with a variety of related methods on several datasets, and the experimental results demonstrate that this new method can significantly improve the classification performance.

In the context of distribution inconsistent data classification, addressing distribution shift is crucial, typically accomplished through domain adaptation (DA) techniques. Once distributions are aligned between the source and target domains, the problem transforms into a conventional recognition task. This article introduces a new method called multitarget DA based on Evidence Theory (MET). For a given target domain, a random merger with other target domains is performed, generating distinct new target domains. Domain-invariant features corresponding to each new target domain are learned by minimizing distribution discrepancies separately between the source and different new target domains. The merging of target domains alters the distribution of the new target domain, leading to variations in the retained information within the learned domain-invariant features. For a query pattern in this target domain, multiple soft classification results (CCR) are obtained after aligning the distributions of the source and different new target domains. These soft CCR complement each other, and evidence theory is employed as a tool to represent and combine uncertain information, fusing these results. The weights for this fusion are automatically learned by minimizing the mean squared error between the combined results and the ground truth on labeled source domain data. The final class decision is determined through the weighted evidential combination of multiple pieces of soft CCR. MET is assessed on several datasets (i.e., Office+Caltech-10, VLSC, and V-RSIR) and compared to various advanced DA methods (e.g., GNN, MT, PAL, PTD, and so on) to validate its effectiveness. The experimental results demonstrate that MET usually can obtain a higher classification performance (i.e., the accuracy can be improved by 2% compared to many methods in most cases).

Synthetic aperture radar (SAR) target recognition based on deep convolutional neural networks (CNN) has achieved great success. Whereas, CNN usually needs massive labeled data to learn. In some cases, it may be difficult to collect a large number of labeled data, especially in SAR target recognition field. Thus, we develop a new method called SAR target recognition based on convolutional feature aggregation and decision combination (FADC) to improve the classification accuracy when labeled SAR data is limited. In FADC, we propose to concatenate the feature maps of different convolutional layers to extract discriminative feature. Then, the first-order statistical features of different layers are used to train extra classifiers. We can obtain two pieces of soft classification results yielded by softmax layer and extra classifiers for a query SAR target image. These soft classification results are combined by weighted arithmetic average rule whose weights are learnt by minimizing the mean squared error between fusion results and ground truth on labeled SAR target images. FADC was tested on MSTAR dataset, and the experiment results demonstrate that it can effectively improve the classification accuracy compared with a variety of advanced methods.

For cross-domain pattern classification, the supervised information (i.e., labeled patterns) in the source domain is often employed to help classify the unlabeled target domain patterns. In practice, multiple target domains are usually available. The unlabeled patterns (in different target domains) which have high-confidence predictions, can also provide some pseudo-supervised information for the downstream classification task. The performance in each target domain would be further improved if the pseudo-supervised information in different target domains can be effectively used. To this end, we propose an evidential multi-target domain adaptation (EMDA) method to take full advantage of the useful information in the single-source and multiple target domains. In EMDA, we first align distributions of the source and target domains by reducing maximum mean discrepancy (MMD) and covariance difference across domains. After that, we use the classifier learned by the labeled source domain data to classify query patterns in the target domains. The query patterns with high-confidence predictions are then selected to train a new classifier for yielding an extra piece of soft classification results of query patterns. The two pieces of soft classification results are then combined by evidence theory. In practice, their reliabilities/weights are usually diverse, and an equal treatment of them often yields the unreliable combination result. Thus, we propose to use the distribution discrepancy across domains to estimate their weighting factors, and discount them before fusing. The evidential combination of the two pieces of discounted soft classification results is employed to make the final class decision. The effectiveness of EMDA was verified by comparing with many advanced domain adaptation methods on several cross-domain pattern classification benchmark datasets.

Abstract In this paper, a secure plaintext‐related image encryption scheme based on compressive sensing and a tent‐sine system is proposed. First, the discrete wavelet transform (DWT) is used to transform the plain image to get a coefficient matrix. Second, several chaotic sequences generated by the tent‐sine chaotic map are used to scramble the coefficient matrix and construct a measurement matrix. Afterward, compressive sensing is performed on the coefficient matrix to obtain a small‐sized encrypted image. Finally, an image encryption scheme related to plaintext is designed. In particular, the proposed system uses the original image information to participate in the encryption process, ensuring the high sensitivity of the cryptosystem to minor differences in the plain image and good performance on resisting known/selected plaintext attacks. Furthermore, to convey the plaintext‐related parameters to the receiver, the dimension of the ciphertext image is expanded, and the parameters are embedded into the ciphertext image. Simulation results and security analysis show that the proposed image encryption system has strong plaintext sensitivity and robustness for effectively resisting various typical attacks such as brute‐force attacks, statistical attacks, and differential attacks.

In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to domain consistency. Therefore, the multiple classification results are discounted by the corresponding weights under belief functions framework, and then, Dempster's rule is employed to combine these discounted results. In order to reduce errors, a neighborhood-based cautious decision-making rule is developed to make the class decision depending on the combination result. The object is assigned to a singleton class if its neighborhoods can be (almost) correctly classified. Otherwise, it is cautiously committed to the disjunction of several possible classes. By doing this, we can well characterize the partial imprecision of classification and reduce the error risk as well. A unified utility value is defined here to reflect the benefit of such classification. This cautious decision-making rule can achieve the maximum unified utility value because partial imprecision is considered better than an error. Several real data sets are used to test the performance of the proposed method, and the experimental results show that our new method can efficiently improve the classification accuracy with respect to other related combination methods.

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.