Supervised Rank Normalization with Training Sample Selection
Gyeongyong Heo, Hun Choi, Joosang Youn · Journal of the Korea Society of Computer and Information · 2015
Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature ∙제1저자 · 교신저자 : 허경용 ∙투고일 : 2014. 11. 3, 심사일 : 2014. 12. 16, 게재확정일 : 2015. 1. 6. * 동의대학교 전자공학과 (Dept. of Electronic Engineering, Dong-eui University) ** 동의대학교 멀티미디어공학과 (Dept. of Multimedia Engineering, Dong-eui University) ※ 이 논문은 2013년 동의대학교 교내연구비 지원으로 연구되었음 (과제번호:2013AA140) 22 Journal of The Korea Society of Computer and Information January 2015 normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method. ▸