Abstract
A new multiple-instance learning (MIL) algorithm which combines local distribution feature of samples with Citation-kNN is proposed. The local distance feature and local sparseness feature are considered. The voters are weighted according to their local distribution. The differently weighted schemes and their combinations are applied to Musk benchmark data set and Breast Ultrasound (BUS) Images. For musk data set, a bag represents a molecule. And instances in a bag represent low-energy conformations of the molecule. For BUS image classification, the image is viewed as a bag and its sub-regions are considered as the instances of the bag. And the image classification problem is converted into a MIL problem. In comparison with Citation-kNN and other methods, the proposed algorithm demonstrates competitive classification accuracy and adaptability.
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 callMore From: Optik - International Journal for Light and Electron Optics
Disclaimer: 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.
