TY - GEN
T1 - Instance selection based on sample entropy for efficient data classification with ELM
AU - WANG, Xizhao
AU - MIAO, Qing
AU - ZHAI, Mengyao
AU - ZHAI, Junhai
N1 - This research is supported by the national natural science foundation of China (61170040), by the natural science foundation of Hebei Province (F2010000323, F2011201063), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD2010139), and by the natural science foundation of Hebei University (2011-228).
PY - 2012
Y1 - 2012
N2 - Instance selection also named sample selection is an important preprocessing step for pattern classification. Almost all of the existing instance selection methods are developed for specific classifiers, such as nearest neighbor (NN) classifier, support vector machine (SVM) classifier. Few of them are designed for single hidden layer feed-forward neural networks (SLFNs) classifier. Based on sample entropy, this paper presents an instance selection method for efficient data classification with extreme learning machine (ELM), which is used to train a SLFN. The proposed method is compared with four state-of-the-art approaches by a series of experiments. The experimental results show that the proposed method can provide similar generalization performance but lower computation time complexity.
AB - Instance selection also named sample selection is an important preprocessing step for pattern classification. Almost all of the existing instance selection methods are developed for specific classifiers, such as nearest neighbor (NN) classifier, support vector machine (SVM) classifier. Few of them are designed for single hidden layer feed-forward neural networks (SLFNs) classifier. Based on sample entropy, this paper presents an instance selection method for efficient data classification with extreme learning machine (ELM), which is used to train a SLFN. The proposed method is compared with four state-of-the-art approaches by a series of experiments. The experimental results show that the proposed method can provide similar generalization performance but lower computation time complexity.
KW - ELM
KW - instances selection
KW - large database
KW - sample entropy
UR - http://www.scopus.com/inward/record.url?scp=84872408300&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2012.6377854
DO - 10.1109/ICSMC.2012.6377854
M3 - Conference paper (refereed)
AN - SCOPUS:84872408300
SN - 9781467317146
T3 - IEEE International Conference on Systems, Man and Cybernetics
SP - 970
EP - 974
BT - Proceedings : 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PB - IEEE
T2 - 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Y2 - 14 October 2012 through 17 October 2012
ER -