5 citations · 7 across the 27 of their papers we have counts for
27 papers
Quantum Incremental Learning with Mixed State Prototypes
Yu Wu, Qianli Zhou, Xinyang Deng +3
Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints. In the Noisy Int…
EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling
Qian Yin, Jiaxing Li, Jiaqi Cheng +23
Earth observation satellite imaging scheduling is a challenging NP-hard combinatorial optimisation problem central to space mission operations. While next-generation agile Earth ob…
Informed Machine Learning with Knowledge Landmarks
Chuyi Dai, Witold Pedrycz, Suping Xu +2
Informed Machine Learning has emerged as a viable generalization of Machine Learning (ML) by building a unified conceptual and algorithmic setting for constructing models on a unif…
Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction
Guangyin Jin, Xiaohan Ni, Yanjie Song +4
With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-valu…
Theoretical Convergence of SMOTE-Generated Samples
Firuz Kamalov, Hana Sulieman, Witold Pedrycz
Imbalanced data affects a wide range of machine learning applications, from healthcare to network security. As SMOTE is one of the most popular approaches to addressing this issue,…
SFS: Spatially-Aware Separability-Driven Feature Selection in Fuzzy Decision Systems
Suping Xu, Chuyi Dai, Ye Liu +3
Feature selection is crucial for fuzzy decision systems (FDSs), as it identifies informative features and eliminates rule redundancy, thereby enhancing predictive performance and i…
HyColor: An Efficient Heuristic Algorithm for Graph Coloring
Enqiang Zhu, Yu Zhang, Haopeng Sun +4
The graph coloring problem (GCP) is a classic combinatorial optimization problem that aims to find the minimum number of colors assigned to vertices of a graph such that no two adj…
MAMBO-NET: Multi-Causal Aware Modeling Backdoor-Intervention Optimization for Medical Image Segmentation Network
Ruiguo Yu, Yiyang Zhang, Yuan Tian +3
Medical image segmentation methods generally assume that the process from medical image to segmentation is unbiased, and use neural networks to establish conditional probability mo…
Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification
Suping Xu, Lin Shang, Keyu Liu +3
Fuzzy rough feature selection (FRFS) is an effective means of addressing the curse of dimensionality in high-dimensional data. By removing redundant and irrelevant features, FRFS h…
LGBQPC: Local Granular-Ball Quality Peaks Clustering
Zihang Jia, Zhen Zhang, Witold Pedrycz
The density peaks clustering (DPC) algorithm has attracted considerable attention for its ability to detect arbitrarily shaped clusters based on a simple yet effective assumption.…
From Distance to Direction: Structure-aware Label-specific Feature Fusion for Label Distribution Learning
Suping Xu, Chuyi Dai, Lin Shang +3
Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), construc…
On the resolution and linear optimization problems subject to a system of bipolar fuzzy relational equalities defined with continuous Archimedean t-norms
Amin Ghodousian, Mohammad Sedigh Chopannavaz, Witold Pedrycz
This paper considers the linear objective function optimization with respect to a more general class of bipolar fuzzy relational equations, where the fuzzy compositions are defined…
Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization
Jie Zhao, Kang Hao Cheong, Witold Pedrycz
Graph-structured combinatorial challenges are inherently difficult due to their nonlinear and intricate nature, often rendering traditional computational methods ineffective or exp…
A note on the theoretical approach to Grassmannians and Plücker coordinates for additive skew-symmetric pairwise comparisons matrices
Waldemar W. Koczkodaj, Witold Pedrycz, Alexander Pigazzini +2
Symmetry and antisymmetry are fundamental concepts in many strict sciences. Pairwise comparisons (PC) matrices are fundamental tools for representing pairwise relations in decision…
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Saidul Islam, Gaith Rjoub, Hanae Elmekki +3
This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from…
Linguistic Fuzzy Information Evolution with Random Leader Election Mechanism for Decision-Making Systems
Qianlei Jia, Witold Pedrycz
Linguistic fuzzy information evolution is crucial in understanding information exchange among agents. However, different agent weights may lead to different convergence results in…
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting
Zhuolin Li, Zhen Zhang, Witold Pedrycz
This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling…
A robust three-way classifier with shadowed granular-balls based on justifiable granularity
Jie Yang, Lingyun Xiaodiao, Guoyin Wang +4
The granular-ball (GB)-based classifier introduced by Xia, exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and fle…
Dual-pronged deep learning preprocessing on heterogeneous platforms with CPU, Accelerator and CSD
Jia Wei, Xingjun Zhang, Witold Pedrycz +2
For image-related deep learning tasks, the first step often involves reading data from external storage and performing preprocessing on the CPU. As accelerator speed increases and…
Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity
Zihang Jia, Zhen Zhang, Witold Pedrycz
Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clu…
DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects
Da Fu, Mingfei Rong, Eun-Hu Kim +2
Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-curre…
Service Level Agreements and Security SLA: A Comprehensive Survey
Serena Nicolazzo, Antonino Nocera, Witold Pedrycz
A Service Level Agreement (SLA) is a formal contract between a service provider and a consumer, representing a crucial instrument to define, manage, and maintain relationships betw…
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
Jia Wei, Xingjun Zhang, Witold Pedrycz
Bagging has achieved great success in the field of machine learning by integrating multiple base classifiers to build a single strong classifier to reduce model variance. The perfo…
A quantitative pairwise comparison-based constraint handling technique for constrained optimization
Ting Huang, Qiang Zhang, Witold Pedrycz +1
This study proposes a new constraint handling technique for assisting metaheuristic optimization algorithms to solve constrained optimization problems more effectively and efficien…