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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| Vydané v: | Procedia computer science Ročník 54; s. 405 - 411 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
2015
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| ISSN: | 1877-0509, 1877-0509 |
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| 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. |
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| AbstractList | 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. |
| Author | Baskaran, R. Abirami, S. Devi, P.R. Suganya |
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| Keywords | Multi-label learning Cure clustering algorithm Class-based features Document classification Label correlations |
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| SubjectTerms | Class-based features Cure clustering algorithm Document classification Label correlations Multi-label learning |
| Title | Multi-Label Learning with Class-Based Features Using Extended Centroid-Based Classification Technique (CCBF) |
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