Simple-random-sampling-based multiclass text classification algorithm.
Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web doc...
| Publicado en: | Scientific World Journal pp. 517498 - 517499 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103820617&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103820617 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103820617 NLM24778587 2012565564 10.1155/2014/517498 NLM24778587 PMC3977423 103820617 ppf: 517498 ppct: 1 formats: tig: atl: Simple-random-sampling-based multiclass text classification algorithm. aug: au: Liu, Wuying Wang, Lin Yi, Mianzhu affil: Department of Language Engineering, PLA University of Foreign Languages, Luoyang, Henan 471003, China ; College of Computer, National University of Defense Technology, Changsha, Hunan 410073, China. sug: subj: Algorithms Models, Theoretical ab: Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm. Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems. The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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