Semi-supervised nearest neighbor editing

—This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data editing technique and we implement our approach based on it. Our approach is termed semi-supervised nearest neighbor editing (SSNNE). Our empirical evaluation using 14 UCI datasets shows that SSNNE outperforms KNN and Wilson editing in terms of generalization ability.

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Donghai Guan, Weiwei Yuan, Young-Koo Lee, Sungyoung Lee
• Keywords: Artificial Intelligence, Data Editing, Data Editing Methods, IJCNN 2008, Wilson Editing