Seizure prediction are necessary for epileptic patients. The global spatial interactions among channels, and long-range temporal dependencies play a crucial role in seizure onset prediction. In addition, it is necessary to search for seizure prediction features in a vast space to learn new generalized feature representations. Many previous deep learning algorithms have achieved some results in automatic seizure prediction. However, most of them do not consider global spatial interactions among channels and long-range temporal dependencies together, and only learn the feature representation in the deep space. To tackle these issues, in this study, an novel bi-level programming seizure prediction model, B2-ViT Net, is proposed for learning the new generalized spatio-temporal long-range correlation features, which can characterize the global interactions among channels in spatial, and long-range dependencies in temporal required for seizure prediction. In addition, the proposed model can comprehensively learn generalized seizure prediction features in a vast space due to its strong deep and broad feature search capabilities. Sufficient experiments are conducted on two public datasets, CHB-MIT and Kaggle datasets. Compared with other existing methods, our proposed model has shown promising results in automatic seizure prediction tasks, and provides a certain degree of interpretability.

Concepts have been adopted in concept-cognitive learning (CCL) and conceptual clustering for concept classification and concept discovery. However, the standard CCL algorithms are incapable of tackling continuous data directly, and some standard conceptual clustering methods mainly focus on the attribute information, ignoring the object information that is also important to improve clustering analysis and concept classification ability. Therefore, in this article, we present a novel concept learning method, called the fuzzy-based concept learning model (FCLM), to address these two issues by exploiting concept hierarchical relations in concept lattices. More specifically, we first show some new related notions for FCLM based on a regular fuzzy formal decision context; among these notions, the object-oriented and attribute-oriented fuzzy concept similarities are used to achieve the concept similarity measure in concept lattices. Moreover, a novel fuzzy concept learning framework is designed, and its corresponding learning algorithms are developed. Finally, we conduct some experiments on various real-world datasets to demonstrate that the proposed method can achieve the state-of-the-art classification performance among similarity-based learning methods. In addition, we further verify the effectiveness of our method in concept discovery on the MNIST dataset.

In human concept learning, people can naturally combine a handful of labeled data with abundant unlabeled data when they make classification decisions, which is also known as semi-supervised learning (SSL) in machine learning. Especially, human concept learning not only is a static process in human cognition but also can vary gradually with dynamic environments. Nevertheless, the classical SSL algorithms must be redesigned to accommodate newly input data. In this sense, concept-cognitive learning may be a good choice, as it can implement dynamic processes by imitating human cognitive processes. Meanwhile, numerous SSL methods were designed based on the feature vector information of instances, while ignoring concept structural information that is a very important process in human knowledge organization. Based on this idea, a novel SSL method, named semi-supervised concept learning method (S2CL), is proposed for dynamic SSL by employing concept spaces, in which knowledge is represented by hierarchical concept structures. Moreover, to make full use of the global and local conceptual information, we further propose an extended version of S2CL (namely, $\text{S2CL}^{\alpha }$ ) for concept learning. More specifically, to effectively exploit the unlabeled data, this paper first shows some new related theories for S2CL (or $\text{S2CL}^{\alpha }$ ) based on a regular formal decision context; then a novel SSL framework is designed, and its corresponding algorithm is developed. Finally, we conduct some experiments on various datasets to demonstrate the effectiveness of our methods, which include concept classification and incremental learning under a large quantity of unlabeled data.