Abstract
Feature subset selection is an important problem in machine learning and data mining. If the suitable features are selected, the results of classification or prediction will be more accurate, while if the unsuitable features are used, the results may have no meaningful. This paper presents a method for feature subset selection that uses the ensemble technique to increase the efficiency of feature selection. Association rule mining is introduced to select the high relationship features. Bagging concept is applied to increase the confidence of selection. The experimental results show the efficiency of the proposed method that outperforms the efficiency of simple association feature subset selection.
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.
