TY - GEN
T1 - An improved KNN algorithm of intelligent built-in test
AU - Dongchao, Ji
AU - Bifeng, Song
AU - Fei, Han
PY - 2008
Y1 - 2008
N2 - Aimed at the faults of K-nearest neighbor (KNN) algorithm in complex equipment's built-in test (BIT), an improved KNN (IKNN) algorithm is proposed to solve the problem from two aspects. Firstly, the weight of each input feature is learned using neural network to make important features contribute more in the classifications; this improves the precision of classification. Secondly, clustering each sample of the training set to reduce the data volume of training set, this improves the running speed of the algorithm. Simulation experiments prove the effectiveness of the IKNN algorithm with higher precision and less calculation.
AB - Aimed at the faults of K-nearest neighbor (KNN) algorithm in complex equipment's built-in test (BIT), an improved KNN (IKNN) algorithm is proposed to solve the problem from two aspects. Firstly, the weight of each input feature is learned using neural network to make important features contribute more in the classifications; this improves the precision of classification. Secondly, clustering each sample of the training set to reduce the data volume of training set, this improves the running speed of the algorithm. Simulation experiments prove the effectiveness of the IKNN algorithm with higher precision and less calculation.
UR - http://www.scopus.com/inward/record.url?scp=49249129781&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2008.4525257
DO - 10.1109/ICNSC.2008.4525257
M3 - 会议稿件
AN - SCOPUS:49249129781
SN - 9781424416851
T3 - Proceedings of 2008 IEEE International Conference on Networking, Sensing and Control, ICNSC
SP - 442
EP - 445
BT - Proceedings of 2008 IEEE International Conference on Networking, Sensing and Control, ICNSC
T2 - 2008 IEEE International Conference on Networking, Sensing and Control, ICNSC
Y2 - 6 April 2008 through 8 April 2008
ER -