Name
Abstract | ||
|---|---|---|
We propose a fuzzy based method for multilabel text classification in which a document can belong to one or more than one category. In text categorization, the number of the involved features is usually huge, causing the curse of the dimensionality problem. Besides, a category can be a nonconvex region, which is a union of several overlapping or disjoint subregions. An automatic classification system, thus, may suffer from large memory requirements or poor performance. By incorporating fuzzy techniques, our proposed method can overcome these issues. A fuzzy relevance measure is adopted to transform high-dimensional documents to low-dimensional fuzzy relevance vectors to avoid the curse of dimensionality problem. A clustering technique is used to divide the relevance space into a collection of subregions which are then combined to make up individual categories. This allows complex and nonconvex regions to be created. A number of experiments are presented to show the effectiveness of the proposed method in both performance and speed. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TFUZZ.2013.2294355 | IEEE T. Fuzzy Systems |
Keywords | Field | DocType |
fuzzy set theory,pattern clustering,fuzzy relevance,fuzzy techniques,multilabel text categorization,fuzzy relevance measure,dimensionality reduction,text classification,fuzzy relevance clustering,clustering,multilabel learning,text analysis,fuzzy based method,clustering technique,fuzzy relevance vectors,curse-of-dimensionality problem | Data mining,Fuzzy clustering,Neuro-fuzzy,Disjoint sets,Fuzzy classification,Fuzzy logic,Curse of dimensionality,Artificial intelligence,FLAME clustering,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 6 | 1063-6706 |
Citations | PageRank | References |
11 | 0.55 | 38 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shie-Jue Lee | 1 | 48 | 5.11 |
| Jung-Yi Jiang | 2 | 109 | 6.21 |