Presentation 2017-10-12
Generalized Subclass Method for Multi-label Classification
Batzaya Norov-Erdene, Mineichi Kudo,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Multi-label classification (MLC) problems are emerging in medical diagnosis, web page annotation, image annotation, etc. Due to the nature of their source data, mostly data being collected time to time, the problem size such as the number of data, the number of features, and the number of classes (labels), becomes so large that conventional algorithms cannot be applied in the limitation of memory and computation time. We solve this scale problem by dividing a large-scale problem into a reasonable number of medium- or small-scale problems.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Multi-label Classification / Subclass method
Paper # PRMU2017-74
Date of Issue 2017-10-05 (PRMU)

Conference Information
Committee PRMU
Conference Date 2017/10/12(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Shinichi Sato(NII)
Vice Chair Hironobu Fujiyoshi(Chubu Univ.) / Yoshihisa Ijiri(Omron)
Secretary Hironobu Fujiyoshi(AIST) / Yoshihisa Ijiri(NAIST)
Assistant Masato Ishii(NEC) / Yusuke Sugano(Osaka Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Generalized Subclass Method for Multi-label Classification
Sub Title (in English)
Keyword(1) Multi-label Classification
Keyword(2) Subclass method
1st Author's Name Batzaya Norov-Erdene
1st Author's Affiliation Hokkaido University(Hokkaido Univ.)
2nd Author's Name Mineichi Kudo
2nd Author's Affiliation Hokkaido University(Hokkaido Univ.)
Date 2017-10-12
Paper # PRMU2017-74
Volume (vol) vol.117
Number (no) PRMU-238
Page pp.pp.67-72(PRMU),
#Pages 6
Date of Issue 2017-10-05 (PRMU)