Scholar
Yi-Xiao He
Google Scholar ID: AbrkogUAAAAJ
Nanjing University of Chinese Medicine
Citations & Impact
Citations
37
H-index
5
i10-index
1
Publications
7
Co-authors
4
list available
Publications
Resume (English only)
Academic Achievements
- 2024.06: Obtained my Ph.D. degree
- 2024.01: The paper 'Interpreting Deep Forest through Feature Contribution and MDI Feature Importance' is accepted by the CCF-B international journal ACM TKDD 2024
- 2023.12: The paper 'Margin Distribution and Structural Diversity Guided Ensemble Pruning' is accepted by the CCF-B international journal MLJ 2024
- 2022.12: The paper 'Depth is More Powerful than Width with Prediction Concatenation in Deep Forests' is accepted by the CCF-A international conference NeurIPS 2022 as an Oral Presentation
- 2025: The paper 'Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation' is accepted by Information Sciences
- 2024: The paper 'Multi-Class Imbalance Problem: A Multi-Objective Solution' is accepted by Information Sciences
- 2024: The paper 'Interpreting Deep Forest through Feature Contribution and MDI Feature Importance' is accepted by ACM Transactions on Knowledge Discovery from Data
- 2024: The paper 'Margin Distribution and Structural Diversity Guided Ensemble Pruning' is accepted by Machine Learning
- 2022: The paper 'Depth is More Powerful than Width with Prediction Concatenation in Deep Forests' is accepted by Advances in Neural Information Processing Systems 35
- 2022: The paper 'Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution' is accepted by Proceedings of the 17th International Conference on Parallel Problem Solving from Nature
- 2020: The paper '蒙德里安深度森林' is accepted by Journal of Computer Research and Development
Research Experience
- Lecturer in School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine
Background
- Research interests include machine learning and multi-objective optimization. Currently, I mainly focus on ensemble learning methods and Artificial Intelligence in TCM Clinical Data Mining.
