TY - JOUR
T1 - Large margin hierarchical classification with mutually exclusive class membership
AU - Wang, Huixin
AU - Shen, Xiaotong
AU - Pan, Wei
PY - 2011/9
Y1 - 2011/9
N2 - In hierarchical classification, class labels are structured, that is each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In such a situation, the focus has been on how to leverage the interclass relationship to enhance the performance of flat classification, which ignores such dependency. This is critical when the number of classes becomes large relative to the sample size. This paper considers single-path or partial-path hierarchical classification, where only one path is permitted from the root to a leaf node. A large margin method is introduced based on a new concept of generalized margins with respect to hierarchy. For implementation, we consider support vector machines and ψ-learning. Numerical and theoretical analyses suggest that the proposed method achieves the desired objective and compares favorably against strong competitors in the literature, including its flat counterparts. Finally, an application to gene function prediction is discussed.
AB - In hierarchical classification, class labels are structured, that is each label value corresponds to one non-root node in a tree, where the inter-class relationship for classification is specified by directed paths of the tree. In such a situation, the focus has been on how to leverage the interclass relationship to enhance the performance of flat classification, which ignores such dependency. This is critical when the number of classes becomes large relative to the sample size. This paper considers single-path or partial-path hierarchical classification, where only one path is permitted from the root to a leaf node. A large margin method is introduced based on a new concept of generalized margins with respect to hierarchy. For implementation, we consider support vector machines and ψ-learning. Numerical and theoretical analyses suggest that the proposed method achieves the desired objective and compares favorably against strong competitors in the literature, including its flat counterparts. Finally, an application to gene function prediction is discussed.
KW - Difference convex programming
KW - Gene function annotation
KW - Margins
KW - Multi-class classification
KW - Structured learning
UR - http://www.scopus.com/inward/record.url?scp=80655140464&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80655140464&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:80655140464
SN - 1532-4435
VL - 12
SP - 2721
EP - 2748
JO - Journal of Machine Learning Research
JF - Journal of Machine Learning Research
ER -