Information Sciences最新文献

Reinforcement learning (RL) has achieved significant success across various tasks. However, generalizing RL for similar tasks remains a challenge. This study leverages expertise from related tasks to introduce a novel algorithm, Ex-RL, for executing transfer learning in tabular RL. The methodology concentrates on abstracting previous experiences into descriptive data and utilizing such data for similar tasks. The research focuses on classic RL solutions for balancing and anti-balancing, which improve the sample efficiency of the learning process. Studies indicate that weak learners, such as Q-learning, require fewer learning episodes, resulting in a 50% improvement and a higher success rate in the learning process. An online virtual lab was developed to facilitate the execution of the experiments. The code is available at Github.

Recent advancements in multiple kernel learning-based graph clustering methods have demonstrated significant promise in effectively learning a consensus kernel matrix from various candidate kernel matrices. This approach enables the creation of a low-dimensional representation in the kernel space of high-dimensional data through self-expressiveness. However, a key challenge remains in capturing the latent geometric properties embedded within different kernel matrices to enhance data representation. In this paper, we propose a novel method called joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering (JKHR). Our approach integrates an innovative adaptive hypergraph Laplacian regularizer, which is characterized by the fusion of multiple nearest neighbor kernel graphs, into the multiple kernel learning-based graph clustering framework. JKHR jointly and adaptively optimizes both the consensus kernel matrix and the hypergraph Laplacian regularizer to achieve a low-dimensional representation that effectively preserves the intrinsic geometry of the data. Experimental results on both synthetic and real benchmark datasets demonstrate that JKHR outperforms state-of-the-art self-expressiveness-based graph clustering methods as well as traditional clustering techniques.

With the rapid advancement of Web 3.0 and the digital transformation of the tourism industry, online reviews have emerged as a new source of information for potential tourists, making data-driven multi-attribute decision-making feasible. However, the vast number of online reviews significantly increases the complexity of tourists' decision-making. Recognizing the gaps in the current literature, particularly the lack of consideration of tourists' psychological behaviours in their decision-making processes and the inadequate handling of ambiguity and uncertainty in reviews, this study proposes a data-driven intuitionistic fuzzy regret-based three-way decision model (RT-DIFTWD). Specifically, after online reviews are crawled, a satisfaction function based on absolute and relative rationality scenarios with intuitionistic fuzzy sets is established by combining sentiment analysis and regret theory. Moreover, two attribute weight calculation methods that are based on frequency and importance conditions are proposed. A flexible three-way multi-attribute decision-making framework that is suitable for different MADM methods is subsequently proposed for deducing the prioritization and classification of alternatives. Finally, we demonstrate our proposed method through a real application of tourism selection in the Chengdu–Chongqing region. The stability, effectiveness and superiority of the presented method are validated by corresponding experimental studies and a comparative analysis.

Multi-label classification is an extension of single-label classification with generations of multi-output for unseen instances. Label correlation is an essential component in constructing multi-label classifiers. How to optimize the representation of label correlation while preserving the semantics of label-specific remains an uncertain issue. Instead of estimating label correlation by a holistic feature representation, we present an augmented label correlation model by generating multi-granularity label-specific features. Firstly, we devise a mixture distance measure to characterize the closeness of an instance by weighing the Pearson correlation coefficient with cosine similarity. Secondly, we explore the local label-specific relative discrimination by leveraging from both the instance-level and class-level correlation distribution within k nearest neighborhood. Finally, we conduct an information fusion strategy to comprehensively integrate the positive and the negative tendencies at the neighborhood level. Instances with salient positive tendency and compact neighborhood structure receive larger values while receiving smaller values with salient negative tendency and sparse neighborhood structure. With the concatenation of original features and augmented features, we examine the classification performance of the proposed granule correlation-based feature augmentation (GOFA) on well-established second-order multi-label classification methods. Extensive comparisons on thirteen benchmarks demonstrate the statistical superiority of GOFA over state-of-the-art multi-label classifications.

Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibility of cluster centers, which limits their validity in practical applications such as facility location problems. To address this issue, this article introduces a novel Mean Shift algorithm that incorporates reachable distance and an iterative mechanism to accurately locate cluster centers. The proposed algorithm initially labels clustering elements with road network coordinates to facilitate the calculation of reachable distance and the cluster center iterative mechanism. Subsequently, the mean shift vector function is modified to employ reachable distance as the measure of geographic reachable similarity. Unlike existing algorithms, our approach allows for cluster centers to be positioned independently of the clustering elements, guaranteeing geographical accessibility. Through simulation experiments, we demonstrate that our proposed algorithm not only outperforms existing methods in terms of solution quality, but also effectively addresses the limitations of disregarding geographical obstacles and unreachable cluster centers.

With the increasing demand for product quality, Key performance indicator (KPI)-oriented process monitoring plays an important role in modern industrial. Partial least squares (PLS)-based methods are widely adopted for KPI-oriented process monitoring, in which the process variable space is decomposed into a principal component subspace that has a strong correlation with KPIs and a residual subspace unrelated to KPIs, and the two spaces are monitored separately. However, if a KPI-unrelated fault occurs in a variable that has no causal relation with any KPI, but has a spurious correlation with some KPIs because of the existence of confounders, PLS based methods may mis-regard the KPI-unrelated fault as a KPI-related fault. This occurs because as a correlation analysis-based method, PLS cannot discriminate whether a variable is a real cause of KPIs or has a spurious correlation with KPIs. Motivated by this, this paper proposes two causal-weighted PLS methods for KPI-oriented process monitoring by combining the LiNGAM-based causal discovery with PLS, which first calculate causal weights based on the modified LiNGAM algorithm and bootstrap strategy, and then employ the causal weights to reweight the weight vectors of PLS to enhance the influence of causal variables in the KPI-related principal subspace and reduce the influence of spurious correlations. Case studies based on a simulated dataset, Tennessee Eastman Process dataset and field data from finishing rolling mill process show that the proposed methods can significantly reduce the false alarm rate for KPI-unrelated faults (i.e., the probability that a KPI-unrelated fault sample is mis-regarded as a KPI-related fault sample) caused by spurious correlation without significantly compromising the fault detection rate of KPI-related faults.

Rough set-based attribute reduction is a powerful technique for data preprocessing in data mining. Knowledge granularity, as a reliable measure for assessing uncertainty in decision information systems (DIS), finds applicability in attribute reduction within such systems. Nevertheless, the limitation arises from the fact that static attribute reduction methods fail to effectively utilize the information contained in acquired data and promptly update knowledge due to the continuous evolution of data. In addition, existing incremental methods based on knowledge granularity are designed exclusively for symbolic data and lack the capability to handle real-valued data. Inspired by this, our study focuses on the attribute reduction approach for fuzzy decision information systems (FDIS) that encompass object variations by utilizing fuzzy knowledge granularity. Firstly, fuzzy knowledge granulation is defined to quantify uncertainty within FDIS, and utilized to determine the importance of attributes for attribute reduction. Additionally, the incremental mechanisms and attribute reduction algorithms are investigated for adding an object and an object set to FDIS, respectively. Moreover, an explication of how the incremental mechanism for increasing an object set can be viewed as a generalization of the mechanism used for a single object is provided. Finally, comparative experiments on various datasets are conducted to validate the effectiveness and efficiency of the proposed incremental algorithms. The results demonstrate that our algorithms achieve superior classification accuracy and while requiring minimal computing time when compared to the comparative algorithms.

The Competitive Influence Maximization (CIM) problem is a critical issue in viral marketing, focusing on selecting a set of influential individuals, known as seed users, for competitors to maximize their revenue. These seed users have significant sway in social networks and serve as valuable marketing resources provided by the platform. They are often displayed in a certain order launched by the platform and the potential information hidden in the order can profoundly affect the final marketing outcomes. However, current CIM research predominantly emphasizes designing effective algorithms for seed selection while ignoring the impact of the seed order launched by the platform. Therefore, this paper focuses on identifying the optimal seed order to maximize platform revenue in a competitive market environment. Specifically, we introduce a new problem called Order-Sensitive Competitive Revenue Maximization (OSCRM) to investigate the CIM problem from a new practical perspective. We prove the problem to be NP-hard and present a simple greedy algorithm with a 1/3-approximate ratio. To address it more efficiently, we further propose an enhanced greedy algorithm called GMST. This algorithm leverages the maximum spanning tree (MST) and achieves a 1/2-approximate ratio. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed GMST algorithm.

Categorical sequence clustering is vital across various domains; however, the interpretability of cluster assignments presents considerable challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, which complicates the explanation of their outcomes. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages $k-1$ patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.

The use of hydrogen storage technology (HST) as a bridge for producing and utilizing hydrogen energy in the hydrogen industry chain is significant, and its evaluation has attracted the interest of researchers. Since there are different types of HSTs, selecting the most appropriate one requires the participation of plenty of experts with different professional backgrounds, which makes this be modeled as a large group decision-making problem. This paper develops a reliability-driven large group consensus decision-making (LGCDM) method for HST selection using the hesitant fuzzy linguistic terms set (HFLTS) as the evaluation representation format. Specifically, the expertise level of individuals and the reliability of group opinions are measured based on the set variables, and then the dimensionality of large groups is reduced based on the reliability of subgroup opinions. Furthermore, an opinion reliability rating mechanism is designed and, when consensus is not satisfactory, a feedback recommendation mechanism and consensus optimization mechanism are developed for implementation. Finally, the proposed reliability-driven LGCDM approach is applied to the HST selection for THVOW Company, and the comparison with existent related approaches indicates that it not only is practical and reasonable, but also provides a technical path for relevant departments to make decisions on practical issues.