Lift: Multi-Label Learning with Label-Specific Features
Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from multi-label data by manipulating with identical feature set, i.e. the very instance representation of each e...
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| Published in | IEEE transactions on pattern analysis and machine intelligence Vol. 37; no. 1; pp. 107 - 120 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.01.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from multi-label data by manipulating with identical feature set, i.e. the very instance representation of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. In this paper, another strategy to learn from multi-label data is studied, where label-specific features are exploited to benefit the discrimination of different class labels. Accordingly, an intuitive yet effective algorithm named LIFT, i.e. multi-label learning with Label specific Features, is proposed. LIFT firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Comprehensive experiments on a total of 17 benchmark data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms as well as the effectiveness of label-specific features. |
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| AbstractList | Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from multi-label data by manipulating with identical feature set, i.e. the very instance representation of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. In this paper, another strategy to learn from multi-label data is studied, where label-specific features are exploited to benefit the discrimination of different class labels. Accordingly, an intuitive yet effective algorithm named LIFT, i.e. multi-label learning with Label specific Features, is proposed. LIFT firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Comprehensive experiments on a total of 17 benchmark data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms as well as the effectiveness of label-specific features. Multi-label learning deals with the problem where each example is represented by a single instance (feature vector) while associated with a set of class labels. Existing approaches learn from multi-label data by manipulating with identical feature set, i.e. the very instance representation of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. In this paper, another strategy to learn from multi-label data is studied, where label-specific features are exploited to benefit the discrimination of different class labels. Accordingly, an intuitive yet effective algorithm named Lift, i.e. multi-label learning with Label specIfic FeaTures, is proposed. Lift firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Comprehensive experiments on a total of 17 benchmark data sets clearly validate the superiority of Lift against other well-established multi-label learning algorithms as well as the effectiveness oflabel-specific features. |
| Author | Zhang, Min-Ling Wu, Lei |
| Author_xml | – sequence: 1 givenname: Min-Ling surname: Zhang fullname: Zhang, Min-Ling email: zhangml@seu.edu.cn organization: School of Computer Science and Engineering, Southeast University, Nanjing, China – sequence: 2 givenname: Lei surname: Wu fullname: Wu, Lei email: wul@seu.edu.cn organization: School of Computer Science and Engineering, Southeast University, Nanjing, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26353212$$D View this record in MEDLINE/PubMed |
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| CODEN | ITPIDJ |
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| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2015 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2015 |
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| SubjectTerms | Algorithm design and analysis Algorithms Clustering algorithms Correlation Discrimination Intelligence Labels Learning Lift Measurement Pattern analysis Strategy Text categorization Training Vectors |
| Title | Lift: Multi-Label Learning with Label-Specific Features |
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