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 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.
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.
Wuying
W
Liu
College of Humanities and Social Sciences, National University of Defense Technology, Changsha, Hunan 410073, China.
Lin
L
Wang
Department of Language Engineering, PLA University of Foreign Languages, Luoyang, Henan 471003, China.
Mianzhu
M
Yi
D000465:Algorithms; D008962:Models, Theoretical