FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors
| Autor: | Shian-Chi Tsai, Jung-Yi Jiang, Shie-Jue Lee |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Multi-label classification
business.industry Document classification General Engineering Pattern recognition computer.software_genre Fuzzy logic Computer Science Applications k-nearest neighbors algorithm ComputingMethodologies_PATTERNRECOGNITION Text categorization Fuzzy similarity measure Artificial Intelligence Maximum a posteriori estimation Fuzzy similarity Artificial intelligence Data mining business computer Mathematics |
| Zdroj: | Expert Systems with Applications. 39:2813-2821 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2011.08.141 |
| Popis: | We propose an efficient approach, FSKNN, which employs fuzzy similarity measure (FSM) and k nearest neighbors (KNN), for multi-label text classification. One of the problems associated with KNN-like approaches is its demanding computational cost in finding the k nearest neighbors from all the training patterns. For FSKNN, FSM is used to group the training patterns into clusters. Then only the training documents in those clusters whose fuzzy similarities to the document exceed a predesignated threshold are considered in finding the k nearest neighbors for the document. An unseen document is labeled based on its k nearest neighbors using the maximum a posteriori estimate. Experimental results show that our proposed method can work more effectively than other methods. |
| Databáze: | OpenAIRE |
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