2020
DOI: 10.1016/j.neucom.2020.06.035
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Randomized multi-label subproblems concatenation via error correcting output codes

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Cited by 5 publications
(3 citation statements)
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“…This technique effectively reduced the computational cost and complexity, but this method failed to obtain accurate text categorization. Shan et al [2] developed a Randomized Multi-Label Sub problems Concatenation (RMSC) technique for the multi-label classification problems. Using this method, the imbalance issues were tackled in such a way that the diversity between the classifiers was enhanced.…”
Section: Literature Reviewmentioning
confidence: 99%
See 2 more Smart Citations
“…This technique effectively reduced the computational cost and complexity, but this method failed to obtain accurate text categorization. Shan et al [2] developed a Randomized Multi-Label Sub problems Concatenation (RMSC) technique for the multi-label classification problems. Using this method, the imbalance issues were tackled in such a way that the diversity between the classifiers was enhanced.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The RMSC model was developed by Shan et al [2] for the multi-label classification problems, but this method failed to learn the binary classifiers with their relationships together for enhancing the performance diversity.…”
Section: Challengesmentioning
confidence: 99%
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