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 |
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| Jazyk: | angličtina |
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Elsevier Inc
01.06.2022
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| ISSN: | 0888-613X, 1873-4731 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Rastogi (nee Khemchandani), Reshma Jain, Sambhav |
| Author_xml | – sequence: 1 givenname: Reshma surname: Rastogi (nee Khemchandani) fullname: Rastogi (nee Khemchandani), Reshma email: reshma.khemchandani@sau.ac.in – sequence: 2 givenname: Sambhav orcidid: 0000-0001-8835-285X surname: Jain fullname: Jain, Sambhav email: sambhav.sau@gmail.com |
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| Cites_doi | 10.1007/s10994-011-5256-5 10.1016/j.patcog.2004.03.009 10.1016/j.neunet.2018.09.003 10.1109/TKDE.2016.2608339 10.1109/TKDE.2017.2785795 10.1016/j.knosys.2020.106709 10.1016/j.ijar.2018.10.009 10.1016/j.knosys.2019.06.014 10.1016/j.entcs.2014.01.025 10.1109/TNNLS.2016.2544779 10.1016/j.patcog.2006.12.019 10.1016/j.knosys.2020.105703 10.1109/TCYB.2017.2663838 10.1109/TKDE.2011.141 10.1109/TPAMI.2014.2339815 10.1007/BF02868641 10.1007/s10489-018-1156-8 10.1016/j.patcog.2015.10.008 10.1016/j.ijar.2015.06.006 10.1007/s10489-018-1345-5 |
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| Keywords | Label correlation Weighted least squares Multi-label classification Minimax probability machine Second order cone programming problem |
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| SubjectTerms | Label correlation Minimax probability machine Multi-label classification Second order cone programming problem Weighted least squares |
| Title | Multi-label learning via minimax probability machine |
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