TY - GEN
T1 - NRMCS : Noise removing based on the MCS
AU - WANG, Xi-Zhao
AU - WU, Bo
AU - HE, Yu-Lin
AU - PEI, Xiang-Hao
N1 - This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548).
PY - 2008
Y1 - 2008
N2 - MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.
AB - MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.
KW - ICF
KW - MCS
KW - Noise
KW - Representative subset
KW - Sample selection
KW - Wilson Editing
UR - http://www.scopus.com/inward/record.url?scp=57849141632&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2008.4620384
DO - 10.1109/ICMLC.2008.4620384
M3 - Conference paper (refereed)
AN - SCOPUS:57849141632
SN - 9781424420957
T3 - International Conference on Machine Learning and Cybernetics (ICMLC)
SP - 89
EP - 93
BT - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
T2 - 7th International Conference on Machine Learning and Cybernetics, ICMLC
Y2 - 12 July 2008 through 15 July 2008
ER -