Abstract
Abstract: It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.
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.
