Information Sciences: an International Journal

Lung cancer is the cancer with the highest morbidity and mortality rate, and selecting treatment options for early-stage lung cancer patients is of great significance in improving cure rate. To help individuals make rational decisions, the even ...

Federated Bayesian optimization (FBO) has been introduced in recent years to avoid privacy leakage when multiple clients involve in finishing a global optimization task. Parameter-sharing-based FBOs, as one branch of FBOs, however, compromise the ...

• This work explores the combination of compressed sensing and BO to achieve privacy-preserving federated optimization via data sharing. Different from other data-sharing strategies in other privacy-preserving BO work, the proposed ...

• A paradigm crossover-based differential evolution algorithm with search space reduction and diversity exploration (PC-SSRDE) is proposed.
• The PC-SSRDE algorithm obtains the correlation coefficient of each dimension of the problem ...

The optimization of complex problems has always been a difficult task in the realm of evolutionary computation, as complex problems often have a large search space. Adding more dimensions to the decision variables also makes the search space more ...

Future flow prediction in spatiotemporal traffic data is a critical requirement for real-world applications, particularly for multi-feature and large-scale data with intricate forecasting mechanisms and varied predictability. Prior sequence-to-...

In hybrid classification problems, apart from labeled data, some related expert knowledge may also be obtained. If the partial information from these two sources can be jointly used well, the performance may be effectively improved. To this end, ...

In this study, we introduce pairwise comparisons matrix classification based on homology groups of graphs with unique vertices. Algebraic topology transforms a sequence of topological objects (such as graphs associated with pairwise comparison ...

Person search is an extremely challenging task that seeks to identify individuals through joint person detection and person re-identification from uncropped real scene images. Previous studies primarily focus on learning rich features to enhance ...

This article addresses the problem of non-zero sum games for Markov jump multiplayer systems (MJMSs) using the reinforcement Q-learning method. Firstly, the Q-functions for each player are derived from the system states and the control inputs. On ...

This paper studies fixed-time (FxT) quantitative synchronization of multilayered complex dynamic networks (CDNs). First, a new FxT stability theorem is established and two new estimations of the settling time of stability are acquired, which are ...

• A novel criterion for FxT stability of a nonlinear system is established.
• Two types of innovative quantized variable-gain saturated control schemes are given.
• By the improved criterion, several FxT synchronization criteria ...

The aim of inductive temporal link prediction is to forecast future edges associated with nodes unseen during training, which is a crucial task in the field of temporal network analysis. Existing methods mainly make predictions by learning from ...

A novel active isolation method for weak faults based on zonotopes is proposed. First, a zonotopic filter is designed to estimate the system state; subsequently, an auxiliary signal is designed based on the obtained state set, and the auxiliary ...

Gaussian random fuzzy numbers are random fuzzy sets generalizing Gaussian random variables and possibility distributions. They define belief functions on the real line that can be conveniently combined by the product-intersection rule under the ...

• Gaussian random fuzzy numbers (GRFNs) represent evidence about real variables.
• We address the combination of dependent GRFNs with an arbitrary correlation matrix.
• We propose two discounting mechanisms for GRFNs.
• We introduce ...

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and ...

Deep neural networks have demonstrated remarkable efficacy in diverse classification tasks. In this paper, we specifically focus on the predictive performance in deep binary classification problems with the sigmoid loss. Given that sigmoid loss ...

Natural neighbor (NaN) algorithm, as a parameter-free alternative to KNN, is widely used in various fields such as pattern recognition and machine learning. However, the original NaN algorithm only takes the Euclidean distance of the samples as ...

Incremental feature selection methods have gained increasing research attention as they improve the efficiency of feature selection for dynamic datasets. Multigranulation rough set, as an extension of rough set theory, allows for a comprehensive ...

In this study, we address the challenge of label sparsity in time-series classification using semi-supervised learning that effectively leverages numerous unlabeled instances. Our approach introduces a pioneering framework for semi-supervised ...

• We propose a novel masked modeling for semi-supervised time-series classification.
• A dual-temporal encoder is designed to reflect diverse temporal resolutions.
• We introduce a novel loss function to mitigate information loss within ...

In recent years, differentially private clustering has received increasing attention. However, most existing differentially private clustering algorithms cannot achieve better results when handling non-convex datasets. To enhance knowledge ...

In the domain of multi-view multi-label (MVML) learning, features are distributed across various views, each offering multiple semantic representations. While existing approaches aim to balance commonality and complementarity within the view ...

Density Peaks Clustering (DPC) is a well-known clustering technique in the data mining field with fewer parameters as well as no iteration. However, when dealing with datasets containing multiple peaks, DPC may subjectively choose the wrong ...

Federated clustering (FC) is an extension of centralized clustering in federated settings. The key here is how to construct a global similarity measure without sharing private data, since the local similarity may be insufficient to group local ...

Dynamic multi-objective optimization is a relatively challenging problem within the field of multi-objective optimization. Nevertheless, these problems have significant real-world applications. The key to addressing dynamic multi-objective ...

In this article, the finite-time stability is discussed for the novel nonlinear mixed multiagent systems (MASs) with unmodeled dynamics and constraints. Each agent is characterized as a state or output feedback system structured in the nonstrict ...

The clustering model known as Minimum Sum-of-Squares Clustering (MSSC) is widely used, with the popular k-means algorithm serving as its local minimizer. It is well-known that solutions of k-means can result in substantial deviations from the ...

• K-means solutions may be far from the global optimum of MSSC.
• Almost all developed methods to overcome this problem are difficult to implement and usually has a lot of parameters.
• Distributed Random Swap is a global optimization ...

A data-driven method is designed to realize the model-free finite-horizon optimal tracking control (FHOTC) of unknown linear discrete-time systems based on Q-learning in this paper. First, a novel finite-horizon performance index (FHPI) that only ...

In this paper, we investigate bipartite containment control problem of multi-agent systems (MASs) with signed directed graph under adversarial inputs. Firstly, we define the bipartite containment error and establish the equivalence between the ...

Image segmentation allows us to separate an image into distinct, non-overlapping parts by utilizing specific features such as hue, texture, and shape. The technique is prevalent in different domains, including target detection, medical imaging, ...

• We use an IFS to represent uncertainty, combine local information propose a novel image representation method.
• We propose an exponential distance measure for IFSs, and show its superiority by illustrating examples.
• We develop a ...

The task of mining high-utility itemsets in a database given a minimum threshold is attracting more and more interest due to its many applications. Existing algorithms such as the vertical ones have the advantages of high scalability, efficiency ...

Asynchronous stochastic gradient descent (ASGD) is a computationally efficient algorithm, which speeds up deep learning training and plays an important role in distributed deep learning. However, ASGD suffers from the stale gradient problem, i.e.,...