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Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF).
- Source :
- Procedia Computer Science; 2015, Vol. 54, p404-410, 7p
- Publication Year :
- 2015
-
Abstract
- Real world applications, such as news feeds categorization deal with multi-label classification problem, where the objects are associated with multiple class labels and each object is represented by a single instance (feature vector). In this paper, a new algorithm adaptation method called centroid-based multi-label classification using class-based features (CCBF) algorithm has been proposed to tackle the multi-label classification problem. It includes class-based feature vectors generation and local label correlations exploitation. In the testing stage, centroid-based classification algorithm is extended for multi-label classification problem. Experiments on reuters multi-label dataset with 103 labels demonstrate the performance and efficiency of CCBF algorithm and the result is compared with those obtained using other multi-label classification algorithms. The CCBF algorithm obtains competitive F measures with respect to the most accurate algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18770509
- Volume :
- 54
- Database :
- Supplemental Index
- Journal :
- Procedia Computer Science
- Publication Type :
- Academic Journal
- Accession number :
- 108433126
- Full Text :
- https://doi.org/10.1016/j.procs.2015.06.047