查看更多>>摘要：? 2022 Elsevier Inc.At present, some researchers have studied the decision-making methods in a fuzzy β-covering approximation space, which not only can play the advantages of rough set theory in dealing with inaccurate data, but also inherit the ranking function of traditional decision-making methods. However, these methods merely consider the ranking problem in a single decision-maker environment and most of the decision-making problems in reality are group decision-making problems that need to consider multiple opinions. In light of this, in this paper, we propose the concept of fuzzy β-covering group approximation spaces and establish a three-way multi-criteria group decision-making method, which can solve some ranking and classification problems of objects under a group decision-making environment. Based on a fuzzy β-covering group approximation space, we firstly propose two fuzzy β-fitting neighborhoods with pessimistic and optimistic attitudes to construct a fuzzy binary relation between any two objects. Secondly, we introduce an overall loss function to estimate the risk loss of all objects when they choose different behaviors in different states under a group decision-making environment. Subsequently, based on the conditional probability estimation formula and the overall loss function, we propose a three-way group decision-making idea in a fuzzy β-covering group approximation space, which contains eight different decision-making attitudes to meet the preferences of decision-makers. Furthermore, for the ranking and classification performance of our method, we use numerical analysis, comparative analysis and Spearman analysis to illustrate the feasibility and superiority of the method, and take experimental analysis to test the stability of our method.
查看更多>>摘要：? 2022 Elsevier Inc.Short-term trajectory prediction (StTP) for individual metro passengers is of great importance in intelligent transportation systems and real-time security risk management. Existing research efforts still have shortcomings in constructing an interpretable StTP model with limited historical trajectory data for individual metro passengers. This paper thus intends to enhance the existing StTP methods in two aspects: (1) encoding the trajectory sequences with universal and personalized mobility rules to explain the mobility patterns of metro passengers, and (2) constructing an interpretable StTP model with adaptive location-awareness to improve the prediction accuracy. Along this line, this study develops a novel framework for individual metro passengers by integrating diverse mobility patterns with adaptive location-awareness (StTP-ML). Particularly, a trajectory representation approach is proposed to process the observed Wi-Fi probe data and transfer them into structured trajectories. Then, the diverse mobility patterns of individual passengers are discovered, including temporal periodicity, spatial symmetry, and sequence correlation. Finally, an adaptive location-aware model integrated with individual diverse mobility patterns-priority strategy is constructed. To evaluate the proposed StTP-ML model, this research also conducts experiments on real-world passenger trajectory data at a busy metro station in Shanghai, China. The experimental results show that the average accuracy rate of the StTP-ML-E model is improved up to 84%, which outperforms all other baseline models in the test. It demonstrates that the proposed StTP-ML model can integrate more interpretable mobility patterns than the baseline models and provide more accurate trajectories for individual metro passengers in future time periods.
查看更多>>摘要：? 2022 Elsevier Inc.This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings). Frequently, the surrounding information is not considered in the learning process. This information is known beforehand in the absence of unobserved weather conditions and remains constant for the same station. Considering the surrounding information as side information facilitates the generalization for predicting pollutants in new stations, leading to a zero-shot regression scenario. Available methods in zero-shot typically lean towards classificat and are not easily extensible to regression. This paper proposes two zero-shot methods for regression. The first method is a similarity based approach that learns models from features and aggregates them using side information. However, potential knowledge of the feature models may be lost in the aggregation. The second method overcomes this drawback by replacing the aggregation procedure and learning the correspondence between side information and feature-induced models, instead. Both proposals are compared with a baseline procedure using artificial datasets, UCI repository communities and crime datasets, and the pollutants. Both approaches outperform the baseline method, but the parameter learning approach manifests its superiority over the similarity based method.
查看更多>>摘要：? 2022 Elsevier Inc.Recently, transfer learning is a popular method in machine learning, which transfers the knowledge learned from source task into target task. In practice, we can obtain the third-class examples except for the positive samples or negative samples, which are called Universum data, and Universum data can improve the performance of the classifier. In this paper, we propose a dictionarybased transfer learning with Universum data method, named U-DTL. In the proposed method, we first introduce the Universum data into the proposed model by the ?-insensitive loss. We then embed two dictionaries for the source and target domains into a new model, and put forward the similarity constraint for dictionaries between both domains to determine the relationship among samples of source and target domains. Further, we use the gradient-based optimization and SVD algorithm to alternately optimize and update the dictionaries, and utilize Lagrangian function to iteratively optimize the proposed U-DTL model to obtain the classifier. Finally, the statistic result of Wilcoxon-test has shown that the proposed U-DTL method has the better performance than previous methods. And we have conducted extensive experiments on the benchmark datasets to evaluate the performance of the proposed U-DTL method and baselines. The results show that the proposed U-DTL method makes the better performance than previous methods.
查看更多>>摘要：? 2022 Elsevier Inc.While the existing multi-view affinity propagation (AP)-based clustering method inevitably works with more than one random initialization and parameter, a novel algorithm called MVCPMM is proposed from a new perspective to achieve more consistent multi-view clustering results with only one random initialization and one parameter. The proposed virtual function nodes added between the variable nodes of the subgraphs of the AP factor graphs (i.e., individual views), enable the core of MVCPMM to pass mutually supervised smooth messages across different views and subsequently exchange the messages within individual views in a mutually supervised manner to encourage the clustering quality of individual views. In addition to maintaining the cluster diversity of individual views, MVCPMM penalizes the changes in the cluster structures of different views by using mutually supervised smooth messages as bidirectional cross-view messages, which can effectively improve the consensus of exemplars across different views. Experimental results on both synthetic and benchmark multi-view datasets demonstrate the superiority of MVCPMM in contrast to several state-of-the-art multi-view clustering methods in terms of both clustering performance and clustering consistency across different views.
查看更多>>摘要：? 2022 Elsevier Inc.An effective Community Scoring Function (CSF) is very important since it can properly quantify the community quality of the node groups and help us to effectively discover valuable network communities. Currently, researchers have proposed many types of CSFs. However, none of them are based on an experimental and theoretical analysis of the node groups with different scales and community qualities. This may significantly weaken their effectiveness. Besides, there are few experiments to comprehensively analyze the effectiveness of the existing CSFs. In this paper, we try to make up for these shortcomings. We analyze the node groups with different scales and community qualities in real-world networks, and find effective functions to measure internal and external connection densities of the groups. We then obtain a novel and robust CSF called ECOQUG by effectively combining two kinds of functions that we determined. We design extensive experiments to contrast the performance of ECOQUG with 13 commonly used CSFs. The final experiments show that ECOQUG performs best among them, which demonstrates its reliability. Furthermore, in order to further demonstrate the necessity and significance of an effective CSF, we optimize the TCE algorithm by applying ECOQUG and an improved expansion method, and produce a new Local Community Detection (LCD) algorithm called Max-ECOQUG. We compare Max-ECOQUG with TCE and other better designed LCD algorithms with different CSFs. The results show that the CSF has a greater impact on the performance of LCD algorithms than the design method, and an effective CSF is necessary and significant.
查看更多>>摘要：? 2022Limited by the shallow structure, broad learning system (BLS) suffers from the high-dimensional data classification difficulty. To this end, the cascade of the convolutional feature mappings and enhancement mappings broad learning system (CCFEBLS) framework is proposed from the perspective of representation learning in this article. Firstly, convolution kernels are exploited to construct the convolutional feature nodes and enhancement nodes instead of using sparse auto-enocder or linear combination in the BLS. Secondly, we design a novel broad and deep architecture which cascades the feature mappings and enhancement mappings as the broad and deep representations to connect the output directly for the CCFEBLS framework. This architecture utilizes all representations thoroughly and improves the representation learning capability. Moreover, to boost the robustness of the CCFEBLS, the weighted hyper-parameters and the group regularization are developed to adjust the broad and deep representations and require the group output directly approximate the label, respectively. And the experimental results on several synthetic and real world datasets have demonstrated that CCFEBLS models outperform the baselines with better performance, less parameters and training time, which are validated to be consistent with the model design and analysis.
查看更多>>摘要：? 2022 Elsevier Inc.This article investigates an adaptive finite-time tracking control for under-actuated nonlinear systems with unknown backlash-like hysteresis and arbitrary switchings. A hierarchical sliding mode (HSM) control scheme is developed considering external disturbances and hysteresis nonlinearity. In addition, unknown functions of the considered system are handled based on neural networks (NNs) approximation, and a projection algorithm is introduced to solve the singularity of the denominator. To avoid the case in which the error is repeatedly magnified, the sum of the upper bounds of the approximation error, the external disturbance and the backlash-like hysteresis model nonlinear parts are estimated.By resorting to the Lyapunov stability theory, the boundedness of all signals can be proved under arbitrary switchings. At last, two simulation examples are presented to demonstrate the validity of the proposed approach.
查看更多>>摘要：? 2022 Elsevier Inc.As graphs grow in size, many real-world graphs are difficult to load into the primary memory of a computer. Thus, computing depth-first search (DFS) results (i.e., depth-first order or DFS-Tree) on the semi-external memory model is important to investigate. Semi-external algorithms assume that the primary memory can at least hold a spanning tree T of a graph G and gradually restructure T into a DFS-Tree, which is nontrivial. In this paper, we present a comprehensive study for the semi-external DFS problem. Based on a theoretical analysis of this problem, we introduce a new semi-external DFS algorithm called EP-DFS with a lightweight index N+-index. Unlike traditional algorithms, we focus on addressing such a complex problem efficiently with fewer I/Os, simpler CPU calculations (implementation-friendly), and less random I/O accesses (key-to-efficiency). Extensive experimental evaluations are performed on both synthetic and real graphs, and experimental results confirm that the proposed EP-DFS algorithm markedly outperforms existing algorithms.
查看更多>>摘要：? 2022 Elsevier Inc.This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.
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