Title :
Research on Multi-classification and Multi-label in Text Categorization
Author_Institution :
Coll. of Chinese Language & Culture, Jinan Univ., Guangzhou, China
Abstract :
Aiming at multi-classification and multi-label in text categorization, an apery algorithm is proposed which judges whether document has multi-classification and multi-label by estimating the similarity difference among final classifier values. If the quotient of the biggest category´s classifier value divided by the second biggest category´s classifier value is less than or equal to a threshold, the document belongs to two categories. The optimum threshold is set to 1.4 by experiment, and experiment results demonstrate performance increases by 1.42 percent.
Keywords :
data mining; learning (artificial intelligence); pattern classification; text analysis; apery algorithm; final classifier values; machine learning; multiclassification problem; multilabel problem; optimum threshold; text categorization; Cybernetics; Data mining; Educational institutions; Electronic mail; Humans; Intelligent systems; Machine learning; Man machine systems; Natural languages; Text categorization; multi-classification and multi-label; text categorization; threshold;
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC '09. International Conference on
Conference_Location :
Hangzhou, Zhejiang
Print_ISBN :
978-0-7695-3752-8
DOI :
10.1109/IHMSC.2009.147