Label-specific feature selection and two-level label recovery for multi-label classification with missing labels

Jianghong Ma; Tommy W. S. Chow

doi:10.1016/j.neunet.2019.04.011

ScienceGate Book Chapters

JOURNAL ARTICLE

Label-specific feature selection and two-level label recovery for multi-label classification with missing labels

Jianghong Ma Tommy W. S. Chow

Year: 2019 Journal: Neural Networks Vol: 118 Pages: 110-126 Publisher: Elsevier BV

DOI: 10.1016/j.neunet.2019.04.011

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Feature selection Multi-label classification Classifier (UML) Pattern recognition (psychology) Machine learning Graph Imputation (statistics) Missing data Data mining Theoretical computer science

Metrics

Cited By

2.30

FWCI (Field Weighted Citation Impact)

Refs

0.90

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Text and Document Classification Technologies

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Image and Video Retrieval Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Label-specific feature selection and two-level label recovery for multi-label classification with missing labels

Metrics

Citation History

Topics

Related Documents

Two-level label recovery-based label embedding for multi-label classification with missing labels

Multi-label feature selection with missing labels

Joint multi-label classification and label correlations with missing labels and feature selection

Robust Multi-label Feature Selection with Missing Labels

Learning Label-Specific Features for Multi-Label Classification with Missing Labels