Discriminative Methods for Multi-labeled Classification

Godbole, Shantanu ; Sarawagi, Sunita (2004) Discriminative Methods for Multi-labeled Classification In: Advances in Knowledge Discovery and Data Mining.

Full text not available from this repository.

Official URL: http://doi.org/10.1007/978-3-540-24775-3_5

Related URL: http://dx.doi.org/10.1007/978-3-540-24775-3_5


In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines perform very well for uni-labeled text classification tasks. Multi-labeled classification is a harder task subject to relatively less attention. In the multi-labeled setting, classes are often related to each other or part of a is-a hierarchy. We present a new technique for combining text features and features indicating relationships between classes, which can be used with any discriminative algorithm. We also present two enhancements to the margin of SVMs for building better models in the presence of overlapping classes. We present results of experiments on real world text benchmark datasets. Our new methods beat accuracy of existing methods with statistically significant improvements.

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to Springer Nature Switzerland AG
Keywords:Support Vector Machinep;Document Vector;Discriminative Method;Label Dimension;Patent Dataset
ID Code:128404
Deposited On:20 Oct 2022 06:12
Last Modified:14 Nov 2022 11:29

Repository Staff Only: item control page