Multi-label learning via minimax probability machine

In this paper, we propose Minimax Probability Machine for Multi-label data classification and is termed as Multi-Label Minimax Probability Machine (MLMPM). Based on data mean and covariance information, MLMPM builds a classifier that minimizes an upper bound on the mis-classification probability of...

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Bibliographic Details
Published in:	International journal of approximate reasoning Vol. 145; pp. 1 - 17
Main Authors:	Rastogi (nee Khemchandani), Reshma, Jain, Sambhav
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.06.2022
Subjects:	Label correlation Minimax probability machine Multi-label classification Second order cone programming problem Weighted least squares Label correlation Weighted least squares Multi-label classification Minimax probability machine Second order cone programming problem
ISSN:	0888-613X, 1873-4731
Online Access:	Get full text
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Summary:	In this paper, we propose Minimax Probability Machine for Multi-label data classification and is termed as Multi-Label Minimax Probability Machine (MLMPM). Based on data mean and covariance information, MLMPM builds a classifier that minimizes an upper bound on the mis-classification probability of unseen future data. For capturing label correlation we have considered asymmetric co-occurrency matrix into the model. The proposed model has also been extended to non-linear settings using the Mercer Kernel trick. To accelerate the training procedure, iterative weighted least squares is used to train the underlying optimization model efficiently. Extensive experimental comparisons of our proposed method with related multi-label algorithms on synthetic as well as real world multi-label datasets, along with Amazon rainforest satellite images dataset, prove its efficacy.
ISSN:	0888-613X 1873-4731
DOI:	10.1016/j.ijar.2022.02.002