Information Sciences: an International Journal

• Our algorithm works on incomplete multi-view data with arbitrary missing patterns.

A fundamental assumption underpinning the recent progress in multi-view clustering is the full observation of all views, which rarely holds for real-world data as they often suffer from the absence of some instances in individual ...

Surrogate-assisted multi-objective evolutionary algorithms have been paid much attention to solve expensive multi-objective problems in recent years. However, with the number of objectives increasing, an improper solution may be picked ...

In this paper, we consider high-dimensional regression with a ℓ 0 constraint. Such optimization problems were once thought to be hard to solve, but recent advances in optimization have provided efficient solvers including iterative ...

Witnessing the growth of inaccurate data with uncertainty and ambiguity, the belief rule base system is attracting increasing research interest because of its usage in decision-making problems as a disjunctive paradigm particularly ...

• This work proposes a hybrid water quality prediction method called SVABEG.
• It ...

Accurate and real-time prediction of water quality not only helps to assess the environmental quality of water, but also effectively prevents and controls water quality emergencies. In recent years, neural networks represented by ...

• A novel method is proposed for traffic flow forecasting.
• A novel module is used ...

Traffic flow forecasting is the cornerstone of the development of intelligent transportation systems. Accurate forecasting is conducive to the control and management of urban traffic. However, it is still a challenge to extract the ...

Deep neural network (DNN) based multivariate time series (MTS) forecasting has been widely studied in many domains. The approach has also been successfully applied to product sales forecasting, which is invaluable in the strategic ...

Machine Learning as a Service can provide outsourced deep learning model prediction services to clients that do not have computing resources. Meanwhile, the integrity of the prediction services cannot be guaranteed due to model theft ...

Clustering is an essential task in machine learning, and learning clustering-friendly representation is crucial for clustering performance. Recently, methods that combine contrastive learning with feature decorrelation have ...

• We propose a traceable one-time address scheme.
• We define an anonymous identity ...

Blockchain systems often use public keys or addresses as pseudonym accounts to protect the identity of users. However, as the blockchain system is transparent, an adversary can analyze all the public keys or addresses and obtain some ...

• Propose cluster-based movement strategy of three-way decisions.
• Propose four ...

The movement strategy is a crucial issue of three-way decisions, which transfers objects in the unfavorable region to the favorable region. For object-based movement strategy, each object in the unfavorable region has one particular ...

Over the decades, traditional outlier detectors have ignored the group-level factor when calculating outlier scores for objects in data by evaluating only the object-level factor, failing to capture the collective outliers. To mitigate ...

In a multi-objective particle swarm optimization (MOPSO), the selection strategies of the personal best solution (pBest) for a single particle and the global best solution (gBest) for the whole swarm are two key challenges to balance ...

Optimal set-point control is important in maintaining good control performance of practical industrial processes. Considering the challenges when modeling a complex process, this work presents a data-driven optimal set-point control (...

The probabilistic hesitant fuzzy sets (PHFSs), which have received a lot of attention and have been widely used in many domains, are effective in describing hesitant evaluations. Despite significant progress, entropy and cross-entropy, ...

• Cooperative driving control protocol for heterogeneous nonlinear and uncertain CAVs.

In this work, the leading-tracking control problem for heterogeneous and uncertain nonlinear autonomous vehicles is addressed. These latter communicate their status information over a wireless communication network and engage in ...

• We provide an in-depth analysis of three strategies for stubborn mining and describe it as a Markov decision process. The optimal policy can be obtained by ...

Bitcoin uses a decentralized network of miners and a distributed consensus algorithm to agree on blockchains to process transactions, and designs certain incentive strategy to ensure the system run persistently. However, recent ...

In deep learning, auxiliary modules for model training have become increasingly popular, such as Deep Mutual Learning (DML) and Multi-Scale Dense Convolutional Networks (MSDNet), which can maximize the performance of the model without ...

The extraction of frequent subgraphs is a basic and well studied operation on graphs. Thus, mining frequent graph patterns and problems associated with it is very important. However, the number of frequent subgraphs is potentially ...

The complex and opaque decision-making process of machine learning models restrains the interpretability of predictions and makes them cannot mine results outside of learning experiences. The causality between features and the target ...

Overlap functions, as a class of not necessarily associative novel aggregation functions, have played an important role in the relevant theory and applications involving fuzzy sets and systems. Recently, extension study has become a ...

For some named entities in the Chinese finance domain that are long, with difficult to delineate boundaries and diverse forms of expression, we propose a method based on pretrained language models for named entity recognition with ...

In real applications, the feature set in a relation decision system often varies with time resulting in a dynamic relation decision system where the existing attribute reduction methods become time-consuming and not suitable. How to ...

This work is concerned with the design of state-feedback, and static output-feedback controllers for uncertain discrete-time systems. The reinforcement learning (RL) method is employed and the controller to be designed is considered as ...

• An adaptive consensus model based on feedback mechanism and social interaction is proposed to improve the consensus efficiency.

Many consensus models in social network group decision making (SNGDM) have been reported to obtain a collective solution despite the initial opinions of decision makers (DMs) may be different. However, these models ignore the obstinacy ...

• Multi-objective linear fractional programming problem.
• Two-phase approach.

This paper presents a new method for solving an intuitionistic fuzzy multi-objective linear fractional optimization (IFMOLFO) problem with crisp and intuitionistic fuzzy constraints. Here, all uncertain parameters are represented as ...

• A multi-objective profit-aware recommender system based on swarm intelligence.
• ...

Movie recommender systems are increasingly present in our daily lives, offering content of interest from streaming providers. Objectives in addition to the liking probability can be proposed to provide movie recommendations. However, ...

• A new cost function is exploited.
• The guaranteed cost control problem for T-S ...

This paper researches the robust guaranteed cost (GC) control problem for a class of networked Takagi–Sugeno (T-S) fuzzy systems with local nonlinear segments and multiple quantizations, where the multiple quantizations mean that both ...

In the growing concerns about data privacy and increasingly stringent data security regulations, it is not feasible to directly mine data or share data if the dataset contains private data. Collecting and analyzing data from multiple ...