SV-kNNC: an algorithm for improving the efficiency of k-nearest neighbor

Authors:
Anantaporn Srisawat;Tanasanee Phienthrakul;Boonserm Kijsirikul
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand;Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand;Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate the performance of this algorithm. SV-kNNC is compared with conventional kNN and kNN with two instance reduction techniques: CNN and ENN. The results show that our algorithm provides the best performance, both predictive accuracy and classification time.