Abstract
Feature selection has become a vital issue in data mining and machine learning. But some challenges have been outstanding when trying to improve the performance of feature selection, such as small sample, uncertain classes, complex features, complementation and redundancy between each feature. In this paper, firstly the background of feature selection is introduced. Then we have presented a new perspective to analyze multi-label feature selection and provided typical papers on different classifications. To further analyze these algorithms, evaluation criterion on results of multi-label feature selection is summarized. Finally, some reflects on research directions, future works and conclusions are organized.
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.
