A Novel Template Reduction Approach for the K-Nearest Neighbor Method

• Published in: IEEE transactions on neural networks Vol. 20; no. 5; pp. 890 - 896
• Main Authors: Fayed, H.A., Atiya, A.F.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2009; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Algorithms; Applied sciences; Artificial intelligence; Boundaries; Cellular neural networks; Chains; Classification; Classification algorithms; Computation; Computer science; control theory; systems; Condensing; Connectionism. Neural networks; cross validation; Design engineering; editing; Exact sciences and technology; K -nearest neighbor (KNN); Layout; Mathematics; Medical diagnosis; Nearest neighbor searches; Neural networks; Pattern classification; Prototypes; Satellites; template reduction; Nearest neighbour; Pattern classification; Very large databases; Condensing; Nearest neighbor approximation; Cross validation; Neural network; template reduction; Fast algorithm; K-nearest neighbor (KNN); editing

The K -nearest neighbor (KNN) rule is one of the most widely used pattern classification algorithms. For large data sets, the computational demands for classifying patterns using KNN can be prohibitive. A way to alleviate this problem is through the condensing approach. This means we remove patterns...