Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF)

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-...

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Veröffentlicht in:Procedia computer science Jg. 54; S. 405 - 411
Hauptverfasser: Devi, P.R. Suganya, Baskaran, R., Abirami, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 2015
Schlagworte:
Class-based features
Cure clustering algorithm
Document classification
Label correlations
Multi-label learning
Multi-label learning
Cure clustering algorithm
Class-based features
Document classification
Label correlations
ISSN:1877-0509, 1877-0509
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Zusammenfassung: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2015.06.047