Citation: | ZHANG Jingxiang and WANG Shitong, “A Novel Single-Feature and Synergetic-Features Selection Method by Using ISE-Based KDE and Random Permutation,” Chinese Journal of Electronics, vol. 25, no. 1, pp. 114-120, 2016, doi: 10.1049/cje.2016.01.018 |
C. Lee and D.A. Landgrebe, “Feature extraction based on decision boundaries”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.4, pp.388-400, 1993.
|
W.H. Hsu, “Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning”, Information Sciences, Vol.163, No.17, pp.103- 122, 2004.
|
M. Krzysztof, et al., “Correlation-based feature selection strategy in classification problems”, Int. J. of Application Mathematics Computer Science, Vol.16, No,4, pp.503-511, 2006.
|
J. Zhong and Q.G. Sun, “A novel feature selection method based on probability latent semantic analysis for Chinese text classification”, Chinese Journal of Electronics, Vol.20, No.2, pp.228- 232, 2011.
|
X.F. He, D. Cai and Niyogi P, “Laplacian scores for feature selection”, Proc. of the Neural Information Processing Systems. MIT Press, pp.507-514, 2005
|
W.J. Hu, K.S. Choi, Y.G. Gu, et al, “Minimum or maximum local structure information for feature selection”, Pattern Recognition Letters, Vol,34. pp.527-535, 2013.
|
K. Kira and L.A. Rendell, “A practical method to feature selection”, Proc. of the 9th International Workshop on Machine Leaning, San Francisco, USA, pp.249-256, 1992.
|
I. Kononenko, “Estimating attributes: Analysis and extensions of RELIEF”, Proc. of ECML. Catania, New York, pp.171-182, 1994.
|
Z.H. Deng, F.L. Chung and S.T. Wang, “Robust relief-feature weighing, margin maximization, and fuzzy optimization”, IEEE Transactions on Fuzzy Systems, Vol.18, No.4, pp.726-744, 2010.
|
P. He and F. Long, “Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.8, pp.1226-1238, 2005.
|
M. Girolami and C. He, “Probability density estimation from optimally condensed data samples”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.25, No.10, pp.1253-1264, 2003.
|
N. Kwak and C.H. Choi, “Input feature selection by mutual information based on Parzen window”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.12, pp.1667-1671, 2002.
|
W.S. David, “Parametric statistical modeling by minimum integrated square error”, Technometrics, Vol.43, pp.274-285, 2005.
|
X.M. Wang and S.T. Wang, “Feature ranking by weighting and the ISE criterion of nonparametric estimation”, Journal of Applied Sciences, Vol.9, No.6, pp.1014-1024, 2009.
|
J. Kim and C.D. Scott, “L2 kernel classification”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.10, pp.1822-1831, 2010.
|
K.Q. Shen, C.J. Ong, X.P. Li, et al., “Feature selection via sensitivity analysis of SVM probabilistic outputs”, Machine Learning, Vol.70, No.1, pp.1-20, 2008.
|
J.B. Yang and C.J. Ong, “An effective feature selection method via mutual information estimation”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.42, No.6, pp.1550-1559, 2012.
|
J.B. Yang, K.Q. Shen, C.J. Ong, et al., “Feature selection for MLP neural network: The use of random permutation of probabilistic outputs”, IEEE Transactions on Neural Networks, Vol.20, No.12, pp.1911-1922, 2009.
|
Z.C. Lu, Q. Zheng and Q. Jin, “Constructing rough set based unbalanced binary tree for feature selection”, Chinese Journal of Electronics, Vol.23, No.3, pp.474-479, 2014.
|
E. Parzen, “On estimation of a probability density function and mode source”, Ann. Math. Statist, Vol.33, No.3, pp.1065-1076, 1962.
|
M.D. Marzio and C.C. Taylor, “Kernel density classification and boosting: An L2 analysis”, Statistics and Computing, Statistics and Computing, Vol.15, No.11, pp.113-123, 2005.
|
M. Rosenblatt, “Global measures of deviation for kernel and nearest neighbor density estimates”, Springer Berlin Heidelberg, pp.181-190, 1979.
|
P.K. Pelckmans, et al., “A risk minimization principle for a class of parzen estimators”, Proc. of the Advances in Neural Information Processing Systems, pp.123-130, 2007.
|
P. Meinicke, T. Twellmann and H. Ritter, “Discriminative densities from maximum contrast estimation”, Proc. of the Neural Information Proceeding Systems 15, Vancouver, Canada, pp.985-992, 2002.
|
A.K. Jain, P.W. Robert and J.C. Mao, “Statistical pattern recognition: A review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.4-37, 2000.
|
S.T. Wang, J. Wang and F.L. Chung, “Kernel density estimation, kernel methods, and fast learning in large data sets”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.44, No.1, pp.1-20, 2014.
|
L. Breiman, “Random forests”, Machine Learning, Vol.1, No.45, pp.5-32, 2001.
|
A. Asuncion and D.J. Newman, UCI Machine Learning Repository, Univ. California, Irvine, CA, 2007. Available: http://www.ics.uci.edu/ mlearn/MLRepository.html. 2014-9-8.
|