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Improving
multilabel classification via heterogeneous ensemble
methods
Yujue Wu and Qing Wang
Vol. 12 (2019), No. 6, 1035–1050
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Abstract |
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We consider the task of multilabel classification, where each instance may belong to multiple labels simultaneously. We propose a new method, called multilabel super learner (MLSL), that is built upon the problem transformation approach using the one-vs-all binary relevance method. MLSL is an ensemble model that predicts multilabel responses by integrating the strength of multiple base classifiers, and therefore it is likely to outperform each base learner. The weights in the ensemble classifier are determined by optimization of a loss function via -fold cross-validation. Several loss functions are considered and evaluated numerically. The performance of various realizations of MLSL is compared to existing problem transformation algorithms using three real data sets, spanning applications in biology, music, and image labeling. The numerical results suggest that MLSL outperforms existing methods most of the time evaluated by the commonly used performance metrics in multilabel classification. |
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Keywords
binary relevance, heterogeneous ensemble, multilabel
classification, stacking, super learner
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Mathematical Subject Classification 2010
Primary: 62-07
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Milestones
Received: 25 October 2018
Revised: 18 March 2019
Accepted: 30 March 2019
Published: 3 August 2019
Communicated by Sat N. Gupta
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Authors |
| Yujue Wu | |
| Department of Mathematics Wellesley College Wellesley, MA United States |
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| Qing Wang | |
| Department of Mathematics Wellesley College Wellesley, MA United States |
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