@article{(Open Science Index):https://publications.waset.org/pdf/15879,
	  title     = {Evolutionary Feature Selection for Text Documents using the SVM},
	  author    = {Daniel I. Morariu and  Lucian N. Vintan and  Volker Tresp},
	  country	= {},
	  institution	= {},
	  abstract     = {Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {9},
	  year      = {2008},
	  pages     = {3172 - 3178},
	  ee        = {https://publications.waset.org/pdf/15879},
	  url   	= {https://publications.waset.org/vol/21},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 21, 2008},
	}