Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning

2021 ◽  
pp. 107336
Author(s):  
Reshma Rastogi ◽  
Sayed Mortaza
Keyword(s):  
Structural Learning ◽  
Missing Labels ◽  
Label Correlation

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

2021 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Yaojin Lin ◽  
Qinghua Hu ◽  
Jinghua Liu ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Empirical Studies ◽  
Feature Space ◽  
Training Data ◽  
Data Sets ◽  
Learning Framework ◽  
Feature Spaces ◽  
Public Data ◽  
Margin Distribution ◽  
Label Correlations ◽  
Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

scholarly journals Bayesian network based label correlation analysis for multi-label classifier chain

2021 ◽  
Vol 554 ◽  
pp. 256-275
Author(s):  
Ran Wang ◽  
Suhe Ye ◽  
Ke Li ◽  
Sam Kwong
Keyword(s):  
Correlation Analysis ◽  
Bayesian Network ◽  
Label Correlation

scholarly journals Multi-Label Learning with Global and Local Label Correlation

2018 ◽  
Vol 30 (6) ◽  
pp. 1081-1094 ◽  
Author(s):  
Yue Zhu ◽  
James T. Kwok ◽  
Zhi-Hua Zhou
Keyword(s):  
Global And Local ◽  
Label Correlation

scholarly journals Mixed states on neural network with structural learning

2004 ◽  
Vol 17 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Tomoyuki Kimoto ◽  
Masato Okada
Keyword(s):  
Neural Network ◽  
Structural Learning ◽  
Mixed States

Structural learning of Bayesian networks using local algorithms based on the space of orderings

2010 ◽  
Vol 15 (10) ◽  
pp. 1881-1895 ◽  
Author(s):  
Juan I. Alonso-Barba ◽  
Luis delaOssa ◽  
Jose M. Puerta
Keyword(s):  
Bayesian Networks ◽  
Structural Learning ◽  
Local Algorithms
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