Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph‐based extreme learning machine method (G‐ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

Electroencephalogram (EEG) signal recognition based on machine learning models is becoming more and more attractive in epilepsy detection. For multiclass epileptic EEG signal recognition tasks including the detection of epileptic EEG signals from different blends of different background data and epilepsy EEG data and the classification of different types of seizures, we may perhaps encounter two serious challenges: (1) a large amount of EEG signal data for training are not available and (2) the models for epileptic EEG signal recognition are often so complicated that they are not as easy to explain as a linear model. In this paper, we utilize the proposed transfer learning technique to circumvent the first challenge and then design a novel linear model to circumvent the second challenge. Concretely, we originally combine γ -LSR with transfer learning to propose a novel knowledge and label space inductive transfer learning model for multiclass EEG signal recognition. By transferring both knowledge and the proposed generalized label space from the source domain to the target domain, the proposed model achieves enhanced classification performance on the target domain without the use of kernel trick. In contrast to the other inductive transfer learning methods, the method uses the generalized linear model such that it becomes simpler and more interpretable. Experimental results indicate the effectiveness of the proposed method for multiclass epileptic EEG signal recognition.

When a fixed number of support vector machines (SVMs) are taken as the base learners, an attempt to diversify them should be encouraged to achieve a satisfactory ensemble. In this article, by means of a negative agreement learning (NAL) strategy, a new SVM-based ensemble framework is proposed to simultaneously enhance the diversity of SVMs in the ensemble and suppress the training error of the ensemble. The proposed ensemble framework is theoretically derived to have distinctive merits: 1) the ensemble and each of its individual SVM base learner are trained in a joint manner rather than in an independent manner and 2) the NAL strategy facilitates the formulation of the ensemble of SVMs as one single SVM; thus, abundant advances in the training of SVM can be conveniently applied to the proposed ensemble learning of SVMs and there is no need to design special optimization techniques for the involved ensemble learning. Extensive experimental studies demonstrate the effectiveness of the proposed ensemble framework of SVMs.

Takagi–Sugeno–Kang (TSK) fuzzy inference has been widely used in approximating uncertain nonlinear systems because of its high interpretability and precision. However, TSK fuzzy inference models are usually constructed on data in a supervised manner for classification and regression problems. As the first attempt, a novel Takagi–Sugeno–Kang fuzzy clustering (TSK-FC) method from the perspective of unsupervised learning is presented in this paper. The distinctive characteristic of TSK-FC lies in that it groups data points into different clusters directly by fuzzy inference on the TSK fuzzy rules. Therefore, the comprehensibility of the clustering procedure and clustering results of TSK-FC can be obtained. Specifically, all input features in TSK-FC on the IF-parts of all fuzzy rules are randomly assigned with interpretable linguistic terms, which ensures the interpreta-bility of TSK-FC. In addition, TSK-FC explores the potential clusters in data patterns by iteratively updating K consequent parameter vectors of all fuzzy rules in an unsupervised manner, which can represent the K obtained clusters. Particularly, all data points from the same cluster will be as close as possible to the cluster center, and as far as possible from the other cluster centers in the iterative update procedure. Moreover, the optimization problem of TSK-FC can be transformed into a standard difference of convex functions using the concave–convex procedure (CCCP), which can be easily solved by introducing an iterative algorithm. Experimental results on synthetic and benchmark datasets as well as four case studies validate the effectiveness of the proposed fuzzy rule-based TSK-FC. The statistical results on all experiments indicate the feasibility of TSK-FC as a clustering tool. The code of TSK-FC is available at https://github.com/gusuhang10/TSK-FC .

Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777+/−0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions.