Abstract
Single-class classification (SCC) seeks to distinguish one class of data from the universal set of multiple classes. We propose a SCC method called general MC that estimates an accurate classification boundary of positive class from small positive data using the distribution of unlabeled data. Our theoretical and empirical analyses show that, as long as the distribution of unlabeled data is not highly skewed in the feature space, general MC significantly outperforms other recent SCC methods when the positive data set is highly under-sampled.
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.
