5 citations · 7 across the 27 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Xiao Mao, Zhiguang Cao, Mingfeng Fan +2
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To solve the underlying task scheduling problem, the deep reinforcement learning…