Test-cost-sensitive attribute reduction based on neighborhood rough set. - Hong Zhao 002; Fan Min 001; William Zhu 001 (GrC, 2011)

Rough matroids based on coverings. - Bin Yang 004; Hong Zhao 002; William Zhu 001 (CoRR, 2013)

Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors. - Hong Zhao 002; Fan Min 001; William Zhu 001 (J. Appl. Math., 2013)

Global Best Artificial Bee Colony for Minimal Test Cost Attribute Reduction. - Anjing Fan; Hong Zhao 002; William Zhu 001 (RSKT, 2014)

A Logarithmic Weighted Algorithm for Minimal Test Cost Attribute Reduction. - Junxia Niu; Hong Zhao 002; William Zhu 001 (RSKT, 2014)

Nullity-based matroid of rough sets and its application to attribute reduction. - Aiping Huang; Hong Zhao 002; William Zhu 001 (Inf. Sci., 2014)

Optimal cost-sensitive granularization based on rough sets for variable costs. - Hong Zhao 002; William Zhu 001 (Knowl. Based Syst., 2014)

Feature Selection with Positive Region Constraint for Test-Cost-Sensitive Data. - Jiabin Liu; Fan Min 001; Hong Zhao 002; William Zhu 001 (Trans. Rough Sets, 2014)

Optimal feature subset with positive region constraints. - Junxia Niu; Hong Zhao 002; William Zhu 001 (ICMLC, 2015)

Cost-sensitive decision tree with probabilistic pruning mechanism. - Hong Zhao 002; Xiangju Li; Zilong Xu; William Zhu 001 (ICMLC, 2015)

Parametric Matroid of Rough Set. - Yanfang Liu; Hong Zhao 002; William Zhu 001 (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2015)

A cost sensitive decision tree algorithm with two adaptive mechanisms. - Xiangju Li; Hong Zhao 002; William Zhu 001 (Knowl. Based Syst., 2015)

Fast randomized algorithm with restart strategy for minimal test cost feature selection. - Jingkuan Li; Hong Zhao 002; William Zhu 001 (Int. J. Mach. Learn. Cybern., 2015)

Cost-sensitive feature selection based on adaptive neighborhood granularity with multi-level confidence. - Hong Zhao 002; Ping Wang; Qinghua Hu (Inf. Sci., 2016)

An exponent weighted algorithm for minimal cost feature selection. - Xiangju Li; Hong Zhao 002; William Zhu 001 (Int. J. Mach. Learn. Cybern., 2016)

Test-cost-sensitive attribute reduction on heterogeneous data for adaptive neighborhood model. - Anjing Fan; Hong Zhao 002; William Zhu 001 (Soft Comput., 2016)

Local Bayes Risk Minimization Based Stopping Strategy for Hierarchical Classification. - Yu Wang 106; Qinghua Hu; Yucan Zhou; Hong Zhao 002; Yuhua Qian; Jiye Liang (ICDM, 2017)

Hierarchical Feature Selection with Recursive Regularization. - Hong Zhao 002; Pengfei Zhu 001; Ping Wang; Qinghua Hu (IJCAI, 2017)

A cost sensitive decision tree algorithm based on weighted class distribution with batch deleting attribute mechanism. - Hong Zhao 002; Xiangju Li (Inf. Sci., 2017)

Multi-label feature selection with missing labels. - Pengfei Zhu 001; Qian Xu; Qinghua Hu; Changqing Zhang; Hong Zhao 002 (Pattern Recognit., 2018)

Hierarchical feature extraction based on discriminant analysis. - Xinxin Liu; Hong Zhao 002 (Appl. Intell., 2019)

Cost-sensitive feature selection via the ℓ2, 1-norm. - Hong Zhao 002; Shenglong Yu (Int. J. Approx. Reason., 2019)

Hierarchical feature selection with subtree based graph regularization. - Qianjuan Tuo; Hong Zhao 002; Qinghua Hu (Knowl. Based Syst., 2019)

Fuzzy Rough Set Based Feature Selection for Large-Scale Hierarchical Classification. - Hong Zhao 002; Ping Wang; Qinghua Hu; Pengfei Zhu 001 (IEEE Trans. Fuzzy Syst., 2019)

Cost-sensitive hierarchical classification for imbalance classes. - Weijie Zheng 003; Hong Zhao 002 (Appl. Intell., 2020)

Hierarchical classification with multi-path selection based on granular computing. - Shunxin Guo; Hong Zhao 002 (Artif. Intell. Rev., 2021)

Cost-sensitive hierarchical classification via multi-scale information entropy for data with an imbalanced distribution. - Weijie Zheng 003; Hong Zhao 002 (Appl. Intell., 2021)

Causality-based online streaming feature selection. - Longzhu Li; Yaojin Lin; Hong Zhao 002; Jinkun Chen; Shaozi Li (Concurr. Comput. Pract. Exp., 2021)

Robust hierarchical feature selection with a capped ℓ2-norm. - Xinxin Liu; Hong Zhao 002 (Neurocomputing, 2021)

Hierarchical feature selection with multi-granularity clustering structure. - Shunxin Guo; Hong Zhao 002; Wenyuan Yang (Inf. Sci., 2021)

Robust hierarchical feature selection driven by data and knowledge. - Xinxin Liu; Yucan Zhou; Hong Zhao 002 (Inf. Sci., 2021)

Hierarchical classification of data with long-tailed distributions via global and local granulation. - Hong Zhao 002; Shunxin Guo; Yaojin Lin (Inf. Sci., 2021)

A Recursive Regularization Based Feature Selection Framework for Hierarchical Classification. - Hong Zhao 002; Qinghua Hu; Pengfei Zhu 001; Yu Wang 106; Ping Wang (IEEE Trans. Knowl. Data Eng., 2021)

A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance. - Zeyu Qiu; Hong Zhao 002 (Appl. Intell., 2022)

Small object detection via dual inspection mechanism for UAV visual images. - Gangyi Tian; Jianran Liu; Hong Zhao 002; Wenyuan Yang (Appl. Intell., 2022)

Hierarchical classification based on coarse- to fine-grained knowledge transfer. - Zeyu Qiu; Minjie Hu; Hong Zhao 002 (Int. J. Approx. Reason., 2022)

Few-shot learning based on hierarchical classification via multi-granularity relation networks. - Yuling Su; Hong Zhao 002; Yaojin Lin (Int. J. Approx. Reason., 2022)

Hybrid ResNet based on joint basic and attention modules for long-tailed classification. - Wei Zhao; Yuling Su; Minjie Hu; Hong Zhao 002 (Int. J. Approx. Reason., 2022)

Multi-task convolutional neural network with coarse-to-fine knowledge transfer for long-tailed classification. - Zhengyu Li 003; Hong Zhao 002; Yaojin Lin (Inf. Sci., 2022)

Few-shot learning via relation network based on coarse-grained granulation. - Xiao Jia 015; Yuling Su; Hong Zhao 002 (Appl. Intell., 2023)

Knowledge transfer based hierarchical few-shot learning via tree-structured knowledge graph. - Zhong Zhang; Zhiping Wu; Hong Zhao 002; Minjie Hu (Int. J. Mach. Learn. Cybern., 2023)

Hierarchical few-shot learning based on coarse- and fine-grained relation network. - Zhiping Wu; Hong Zhao 002 (Artif. Intell. Rev., 2023)

Feature selection via maximizing inter-class independence and minimizing intra-class redundancy for hierarchical classification. - Jie Shi; Zhengyu Li 003; Hong Zhao 002 (Inf. Sci., 2023)

Hierarchical Feature Selection Based on Label Distribution Learning. - Yaojin Lin; Haoyang Liu; Hong Zhao 002; Qinghua Hu; Xingquan Zhu 001; Xindong Wu 001 (IEEE Trans. Knowl. Data Eng., 2023)

Hierarchical few-shot learning with feature fusion driven by data and knowledge. - Zhiping Wu; Hong Zhao 002 (Inf. Sci., 2023)

Coarse-to-fine knowledge transfer based long-tailed classification via bilateral-sampling network. - Junyan Xu; Wei Zhao; Hong Zhao 002 (Int. J. Mach. Learn. Cybern., 2023)

FS-MGKC: Feature selection based on structural manifold learning with multi-granularity knowledge coordination. - Jie Shi; Hong Zhao 002 (Inf. Sci., 2023)

ECS-SC: Long-tailed classification via data augmentation based on easily confused sample selection and combination. - Wenwei He; Junyan Xu; Jie Shi; Hong Zhao 002 (Expert Syst. Appl., 2024)

HMRM: Hierarchy-aware Misclassification Risk Minimization for few-shot learning. - Jie Jin; Yangqing Zhong; Hong Zhao 002 (Expert Syst. Appl., 2024)

DMTFS-FO: Dynamic multi-task feature selection based on flexible loss and orthogonal constraint. - Yang Zhang; Jie Shi; Hong Zhao 002 (Expert Syst. Appl., 2024)

Generative Essential Graph Convolutional Network for Multi-View Semi-Supervised Classification. - Jielong Lu; Zhihao Wu 003; Luying Zhong; Zhaoliang Chen; Hong Zhao 002; Shiping Wang (IEEE Trans. Multim., 2024)

Hierarchical long-tailed classification based on multi-granularity knowledge transfer driven by multi-scale feature fusion. - Wei Zhao; Hong Zhao 002 (Pattern Recognit., 2024)

Hierarchical Convolutional Neural Network with Knowledge Complementation for Long-Tailed Classification. - Hong Zhao 002; Zhengyu Li 003; Wenwei He; Yan Zhao (ACM Trans. Knowl. Discov. Data, 2024)

Few-Shot Learning With Multi-Granularity Knowledge Fusion and Decision-Making. - Yuling Su; Hong Zhao 002; Yifeng Zheng; Yu Wang 106 (IEEE Trans. Big Data, 2024)

FSAKE: Few-shot graph learning via adaptive neighbor class knowledge embedding. - Linhua Zou; Jie Jin; Dongqing Li; Hong Zhao 002 (Expert Syst. Appl., 2025)

Multi-granularity awareness via cross fusion for few-shot learning. - Zhiping Wu; Dongqing Li; Linhua Zou; Hong Zhao 002 (Inf. Sci., 2025)

FSPDF: Few-shot learning with progressive dual-domain feature fusion via self-supervised learning. - Dongqing Li; Jie Jin; Linhua Zou; Hong Zhao 002 (Knowl. Based Syst., 2025)

Information-controlled graph convolutional network for multi-view semi-supervised classification. - Yongquan Shi; Yueyang Pi; Zhanghui Liu; Hong Zhao 002; Shiping Wang (Neural Networks, 2025)

Hierarchical feature selection driven by inter-category relevance and semantic independence. - Zilong Lin 002; Yaojin Lin; Yang Zhang; Hong Zhao 002 (Pattern Recognit., 2025)

CSTS: Exploring Class-Specific and Task-Shared Embedding Representation for Few-Shot Learning. - Hong Zhao 002; Yuling Su; Zhiping Wu; Weiping Ding 001 (IEEE Trans. Neural Networks Learn. Syst., 2025)

PHFS: Progressive Hierarchical Feature Selection Based on Adaptive Sample Weighting. - Hong Zhao 002; Jie Shi; Yang Zhang (IEEE Trans. Neural Networks Learn. Syst., 2025)