Unlike single-label document classification, where each document exactly belongs to a single category, when the document is classified into two or more categories, known as multi-label file, how to classify such documents accurately has become a hot research topic in recent years. In this paper, we propose a algorithm named fuzzy similarity measure multi-label K nearest neighbors(FSMLKNN) which combines a fuzzy similarity measure with the multi-label K nearest neighbors(MLKNN) algorithm for multi-label document classification, the algorithm improved fuzzy similarity measure to calculate the similarity between a document and the center of cluster similarity, and proposed algorithm can significantly improve the performance and accuracy for multi-label document classification. In the experiment, we compare FSMLKNN and the existing classification methods, including decision tree C4.5, support vector machine(SVM) and MLKNN algorithm, the experimental results show that, FSMLKNN method is better than others.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0810110-175700 |
| Date | 10 August 2010 |
| Creators | Tsai, Shian-Chi |
| Contributors | none, none, none, none, Shie-Jue Lee |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810110-175700 |
| Rights | unrestricted, Copyright information available at source archive |
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