Survey on angle-based classification
Sheng Fu, Yuan Xue, Sanguo Zhang · 2019
Statistical classification problems are widely encountered in many applications, e.g., face recognition, fraud detection, and hand-written character recognition. In this article we make a comprehensive analysis on statistical methods for supervised classification problems. Specifically, we introduce the angle-based classification structure, which combines binary and multicategory problems in a unified framework. Several new variants of the angle-based classifiers are also discussed, such as robust learning and weighted learning. Furthermore, we show some theoretical results about Fisher consistency for these angle-based classifiers.