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Multi-Label Classification With Label-Specific Feature Generation: A Wrapped Approach.
- Source :
-
IEEE Transactions on Pattern Analysis & Machine Intelligence . Sep2022, Vol. 44 Issue 9, p5199-5210. 12p. - Publication Year :
- 2022
-
Abstract
- Label-specific features serve as an effective strategy to learn from multi-label data, where a set of features encoding specific characteristics of each label are generated to help induce multi-label classification model. Existing approaches work by taking the two-stage strategy, where the procedure of label-specific feature generation is independent of the follow-up procedure of classification model induction. Intuitively, the performance of resulting classification model may be suboptimal due to the decoupling nature of the two-stage strategy. In this paper, a wrapped learning approach is proposed which aims to jointly perform label-specific feature generation and classification model induction. Specifically, one (kernelized) linear model is learned for each label where label-specific features are simultaneously generated within an embedded feature space via empirical loss minimization and pairwise label correlation regularization. Comparative studies over a total of sixteen benchmark data sets clearly validate the effectiveness of the wrapped strategy in exploiting label-specific features for multi-label classification. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CLASSIFICATION
*LEARNING strategies
*TASK analysis
Subjects
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 44
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 158406115
- Full Text :
- https://doi.org/10.1109/TPAMI.2021.3070215