A Classification Method Considering the Neighborhood Information on Observation Points and Its Application to IVUS Data

Abstract

We propose a novel classification method, which utilizes not only the feature vectors but also the neighborhood information on data observation points. The proposed method achieves an effective classification even if the boundary of each class is overlapped in the feature space. The practical effectiveness of the proposed method is verified by applying it to the classification problem of the intravascular ultrasound radiofrequency data.