Ping Zhang

Orcid: 0000-0002-2645-9157

Affiliations:
  • Hebei University of Technology, School of Artificial Intelligence, Hebei Province Key Laboratory of Big Data Calculation, Tianjin, China
  • Jilin University, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering, Changchun, Chian (PhD 2018)
  • Jilin University, College of Software, Changchun, China


According to our database1, Ping Zhang authored at least 34 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
M3S-Net: Multimodal Feature Fusion Network Based on Multi-scale Data for Ultra-short-term PV Power Forecasting.
CoRR, February, 2026

Graph-fusion guided data reconstruction for multi-view multi-label feature selection.
Pattern Recognit., 2026

Multi-Source Information Driven Spatio-Temporal Hypergraph Learning for Traffic Forecasting.
Proceedings of the ACM Web Conference 2026, 2026

2025
MPCM-Net: Multi-scale network integrates partial attention convolution with Mamba for ground-based cloud image segmentation.
CoRR, November, 2025

USF-Net: A Unified Spatiotemporal Fusion Network for Ground-Based Remote Sensing Cloud Image Sequence Extrapolation.
CoRR, November, 2025

LG-Umer: UNet-Like Network Integrate Local-Global Feature With Novel Attention for Road Extraction From Remote Sensing Images.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025

Exploring multi-label feature selection via feature and label information supplementation.
Eng. Appl. Artif. Intell., 2025

2024
MDCGA-Net: Multiscale Direction Context-Aware Network With Global Attention for Building Extraction From Remote Sensing Images.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024

Label generation with consistency on the graph for multi-label feature selection.
Inf. Sci., 2024

2023
MFSJMI: Multi-label feature selection considering join mutual information and interaction weight.
Pattern Recognit., June, 2023

Multi-label feature selection via redundancy of the selected feature set.
Appl. Intell., May, 2023

A unified low-order information-theoretic feature selection framework for multi-label learning.
Pattern Recognit., 2023

2022
Multi-label feature selection method based on dynamic weight.
Soft Comput., 2022

Feature-specific mutual information variation for multi-label feature selection.
Inf. Sci., 2022

2021
Preserving Similarity and Staring Decisis for Feature Selection.
IEEE Trans. Artif. Intell., 2021

Multi-label feature selection considering label supplementation.
Pattern Recognit., 2021

Multi-label feature selection based on the division of label topics.
Inf. Sci., 2021

Multi-Label Feature Selection Combining Three Types of Conditional Relevance.
Entropy, 2021

A conditional-weight joint relevance metric for feature relevancy term.
Eng. Appl. Artif. Intell., 2021

Feature relevance term variation for multi-label feature selection.
Appl. Intell., 2021

2020
Multi-label feature selection with shared common mode.
Pattern Recognit., 2020

Robust multi-label feature selection with dual-graph regularization.
Knowl. Based Syst., 2020

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption.
Entropy, 2020

Feature selection considering Uncertainty Change Ratio of the class label.
Appl. Soft Comput., 2020

Feature redundancy term variation for mutual information-based feature selection.
Appl. Intell., 2020

A Novel Prediction Method for ATP-Binding Sites From Protein Primary Sequences Based on Fusion of Deep Convolutional Neural Network and Ensemble Learning.
IEEE Access, 2020

Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection.
IEEE Access, 2020

2019
Distinguishing two types of labels for multi-label feature selection.
Pattern Recognit., 2019

2018
Feature selection considering the composition of feature relevancy.
Pattern Recognit. Lett., 2018

Class-specific mutual information variation for feature selection.
Pattern Recognit., 2018

Feature selection considering two types of feature relevancy and feature interdependency.
Expert Syst. Appl., 2018

Feature selection by integrating two groups of feature evaluation criteria.
Expert Syst. Appl., 2018

Feature selection considering weighted relevancy.
Appl. Intell., 2018

Feature Selection by Maximizing Part Mutual Information.
Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, 2018


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