Abstract
There are relevance and redundancy of the feature words in the text vector space, so we proposed a text-reducing method based on the improved KNN algorithm in this paper. Vector polymer theory and feature selection methods were used to reducing the dimension of vector space. Feature words would have more ability to represent categories after feature selection. Experiments proved, the improved KNN algorithm were used in text-reducing not only can reducing the dimension of vector space more effectively, but also can improving the speed and accuracy of the text classify.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
