TY - GEN
T1 - SV-kNNC
T2 - 9th Pacific Rim International Conference on Artificial Intelligence
AU - Srisawat, Anantaporn
AU - Phienthrakul, Tanasanee
AU - Kijsirikul, Boonserm
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/33749557678
U2 - 10.1007/11801603_117
DO - 10.1007/11801603_117
M3 - Conference contribution
AN - SCOPUS:33749557678
SN - 3540366679
SN - 9783540366676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 975
EP - 979
BT - PRICAI 2006
PB - Springer Verlag
Y2 - 7 August 2006 through 11 August 2006
ER -