TY  - JOUR
SN  - 2010376X
SP  - 137
ID  - scholars1005
VL  - 72
TI  - A proposed hybrid approach for feature selection in text document categorization
N1  - cited By 1
Y1  - 2010///
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651593676&partnerID=40&md5=c59ed4f89ed18c5bff83ca741a230819
EP  - 141
KW  - Ant-colony optimization; Feature selection; Information gain; Text categorization; Text representation
KW  -  Algorithms; Artificial intelligence; Optimization; Text processing
KW  -  Feature extraction
JF  - World Academy of Science, Engineering and Technology
A1  - Zaiyadi, M.F.
A1  - Baharudin, B.
N2  - Text document categorization involves large amount of data or features. The high dimensionality of features is a troublesome and can affect the performance of the classification. Therefore, feature selection is strongly considered as one of the crucial part in text document categorization. Selecting the best features to represent documents can reduce the dimensionality of feature space hence increase the performance. There were many approaches has been implemented by various researchers to overcome this problem. This paper proposed a novel hybrid approach for feature selection in text document categorization based on Ant Colony Optimization (ACO) and Information Gain (IG). We also presented state-of-the-art algorithms by several other researchers.
ER  -