Abstract
We study the problem of feature selection in text classification. Previous researches use only a measurement such as information gain, mutual information, chi-square for selecting good features. In this paper we propose a new approach to feature selection - dynamic feature selection. A new algorithm for feature selection is proposed. In this algorithm, by combining different measurements for features and tuning parameters, several feature subsets are generated, the best feature set which achieves the best performance from a classifier is obtained from those. Experiments dealing with the real-world data set show that the proposed dynamic feature selection outperforms traditional feature selection methods.
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.
