Missingness-Pattern-Adaptive Learning With Incomplete Data

Author:

Gong Yongshun1ORCID,Li Zhibin2ORCID,Liu Wei3ORCID,Lu Xiankai1ORCID,Liu Xinwang4ORCID,Tsang Ivor W.5ORCID,Yin Yilong1ORCID

Affiliation:

1. School of Software, Shandong University, Zibo, Shandong Province, China

2. Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia

3. Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, Australia

4. College of Computer, National University of Defense Technology, Changsha, China

5. Centre for Frontier AI Research, A*STAR and IHPC, A*STAR, Singapore

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong province, China

Shandong Excellent Young Scientists Fund

Shandong Provincial Natural Science Foundation for Distinguished Young Scholars

Taishan Scholar Project of Shandong Province

Young Elite Scientists Sponsorship Program

Open Fund of Beijing Key Laboratory of Traffic Data Analysis and Mining

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference66 articles.

1. Adjusted weight voting algorithm for random forests in handling missing values

2. Automatic differentiation in pytorch;paszke;Proc Workshop Conf Neural Inf Process Syst,2017

3. Learning to classify with missing and corrupted features

4. Tools from higher algebra;alon;Handbook of Combinatorics,1995

5. Incomplete-data classification using logistic regression

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