In partial label learning (PLL) tasks, each training instance is assigned a candidate label set, with only one label being correct. Previous studies on PLL have focused on scenarios where the class label set remains fixed, i.e., the label set for test data is the same as that used during training. However, in many real-world applications, the environment is dynamic, and new labels may emerge, requiring methods that can detect and classify these new labels. Moreover, previous methods typically learn from partial label data by manipulating the same feature set, which may be suboptimal as it overlooks the semantic relationships between instances and labels. To this end, we develop a novel PLL approach called Label-Specific feature-based Partial label learning with Emerging new Labels (LSPEL), which works by iteratively learning label-specific features during the label disambiguation process to support new label detection and model update. It consists of three key components: (1) model training based on label-specific feature learning, (2) construction of a new label detector that works in conjunction with the classifier to predict known labels, and (3) model updating and induction to further enhance the prediction results for known labels. Extensive experiments on synthetic and real-world PL datasets demonstrate that LSPEL is effective in handling emerging new labels.

Open Set Recognition (OSR) aims to accurately classify known classes and effectively reject unknown classes in open-world scenarios, which is essential for building safe and reliable intelligent systems. Existing studies have shown that auxiliary data-based methods—utilizing outlier exposure or prompt-based guidance to strengthen OSR models—have achieved significant improvements. However, these methods are highly sensitive to the selection of data. Considering the demand for domain expertise and the cost implications, synthesizing data from known samples becomes a preferable alternative. To achieve performance on par with methods that incorporate auxiliary data, it is essential to address the following key challenges: (1) obtaining meaningful pseudo-unknown samples and prompts without manual intervention, and (2) establishing a reliable recognition mechanism during the inference phase. In this paper, we propose a novel Dual-Space Prompt Learning with Context Bias Decoupling (DSPL) method to address the above issues. Specifically, we symmetrically model the same number of unknown classes as known classes to achieve a balanced class distribution in the embedding space. Learnable textual templates are designed for both types of data. Meanwhile, boundary samples are synthesized and treated as pseudo-unknown samples by decoupling discriminative and co-occurring features. Additionally, unknown sample detection is performed by integrating the maximum posterior over known and unknown classes, thereby extending the advantages of symmetric modeling from the training phase to inference. Extensive experiments demonstrate that the proposed method DSPL achieves state-of-the-art performance compared with the most advanced OSR methods.

Hierarchical classification learning, which organizes data categories into a hierarchical structure, is an effective approach for large-scale classification tasks. The high dimensionality of data feature space, represented in hierarchical class structures, is one of the main research challenges. In addition, the class hierarchy often introduces imbalanced class distributions and causes overfitting. In this paper, we propose a feature selection method based on label distribution learning to address the above challenges. The crux is to alleviate the class imbalance problem and learn a discriminative feature subset for hierarchical classification process. Due to correlation between different class categories in the hierarchical tree structure, sibling categories can provide additional supervisory information for each learning sub tasks, which, in turn, alleviates the problem of under-sampling of minority categories. Therefore, we transform hierarchical labels to a hierarchical label distribution to represent this correlation. After that, a discriminative feature subset is selected recursively, by the common features and label-specific feature constraints, to ensure that downstream classification tasks can achieve the best performance. Experiments and comparisons, using seven well-established feature selection algorithms on six real data sets with different degrees of imbalance, demonstrate the superiority of the proposed method.

Multi-label streaming feature selection has attracted extensive attention in diverse big data applications. However, most existing works focused on the scenarios where labels are independent, while ignoring the real scenarios that they may be interdependent and correlated with each other. This paper aims to fill this gap by developing a novel online multi-label streaming feature selection scheme by taking into account the existence of label correlation, known as (OMSFSLC). In our design, we first calculate the correlation degree between labels to obtain the label weight. Then, we integrate the mutual information and the label weight to evaluate the correlation between features and labels. In particular, it consists of three stages: 1) online significance analysis, which can determine the significant features via the correlation degree between the newly arriving features and labels; 2) online relevance analysis, which can obtain relevant features via the mutual information; and 3) online redundancy analysis, which can filter the redundant features for removal via pairwise comparison. We implement our solution and conduct extensive experiments on benchmark datasets for performance evaluations. The experimental results exhibit that OMSFSLCsignificantly outperforms the state-of-the-art methods in terms of effectiveness and efficiency.

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.