Title :
Semi-supervised fuzzy learning in text categorization
Author :
Xin Pan ; Suli Zhang
Author_Institution :
Sch. of Electr. & Inf. Technol., Changchun Inst. of Technol., Changchun, China
Abstract :
Semi-supervised learning has attracted a lot of attention in recent years. Different from traditional supervised learning. Semi-supervised learning makes use of both labeled and unlabeled samples. In text categorization, traditional classifier prefer lots of samples and each category have same number of simples, but collecting labeled examples costs human efforts and certain category may be can´t find abundance samples; this situation would lead to low classification accuracy. In this paper, we proposed a semi-supervised text classifier based on fuzzy c-means algorithm. Experiment show that our method has better performance in small samples and unbalance samples.
Keywords :
data mining; fuzzy reasoning; pattern classification; text analysis; classification accuracy; fuzzy c-means algorithm; semi supervised fuzzy learning; semi supervised text classifier; text categorization; unlabeled samples; Accuracy; Classification algorithms; Computational modeling; Machine learning; Support vector machine classification; Text categorization; Training; Fuzzy C-Means; Semi-supervised; Text Categorization; Unlabeled samples;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019630