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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Vydáno v:International journal of approximate reasoning Ročník 145; s. 1 - 17
Hlavní autoři: Rastogi (nee Khemchandani), Reshma, Jain, Sambhav
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.06.2022
Témata:
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
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Popis
Shrnutí: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