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张鑫 等:自然进化策略的特征选择算法研究 3751
们的算法应用到更多的优化领域,特别是一些可以并行的优化过程中来检验我们算法的性能.同时,尝试将我们
的算法应用到实际的工业领域来考察算法的适用性.
References:
[1] Guyon I, Elisseeff A. An introduction to variable and feature selection. Journal of Machine Learning Research, 2003,1157−1182.
[2] Liu H, Motoda H, Setiono R, et al. Feature selection: An ever evolving frontier in data mining. Journal of Machine Learning
Research, 2010,4−13.
[3] Dash M, Liu H. Feature selection for classification. Intelligent Data Analysis, 1997,1(3):131−156.
[4] Chandrashekar G, Sahin F. A survey on feature selection methods. Computers & Electrical Engineering, 2014,40(1):16−28.
[5] Kohavi R, John GH. Wrappers for feature subset selection. Artificial Intelligence, 1997,97(1-2):273−324.
[6] Tan KC, Teoh EJ, Yu Q, et al. A hybrid evolutionary algorithm for attribute selection in data mining. Expert Systems with
Applications, 2009,36(4):8616−8630.
[7] Bart S, Gomes CP. Hill-climbing Search. Encyclopedia of Cognitive Science, 2006.
[8] Pudil P, Novovicova J, Kittler J, et al. Floating search methods in feature selection. Pattern Recognition Letters, 1994,15(11):
1119−1125.
[9] Moustakidis SP, Theocharis JB. SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary
criterion. Pattern Recognition, 2010,43(11):3712−3729.
[10] Xue B, Zhang M, Browne WN, et al. A survey on evolutionary computation approaches to feature selection. IEEE Trans. on
Evolutionary Computation, 2016,20(4):606−626.
[11] Zhu W, Si G, Zhang Y, et al. Neighborhood effective information ratio for hybrid feature subset evaluation and selection.
Neurocomputing, 2013,99:25−37.
[12] Xue B, Zhang M, Browne WN. Particle swarm optimization for feature selection in classification: A multi-objective approach.
IEEE Trans. on Cybernetics, 2013,43(6):1656−1671.
[13] Tabakhi S, Moradi P, Akhlaghian F, et al. An unsupervised feature selection algorithm based on ant colony optimization.
Engineering Applications of Artificial Intelligence, 2014,112−123.
[14] Ghaemi M, Feizi-Derakhshi MR. Feature selection using forest optimization algorithm. Pattern Recognition, 2016,60:121−129.
[15] Zhang Y, Song XF, Gong DW. A return-cost-based binary firefly algorithm for feature selection. Information Sciences, 2017,418.
[16] Hammami M, Bechikh S, Hung C, et al. A multi-objective hybrid filter-wrapper evolutionary approach for feature selection.
Memetic Computing, 2019,11(2):193−208.
[17] Hancer E. Differential evolution for feature selection: A fuzzy wrapper-filter approach. Soft Computing, 2019,23(13):5233−5248.
[18] Chen YP, Li Y, Wang G, et al. A novel bacterial foraging optimization algorithm for feature selection. Expert Systems with
Applications, 2017,83:1−17.
[19] Qian C, Yu Y, Zhou Z, et al. Subset selection by Pareto optimization. In: Proc. of the Neural Information Processing Systems. 2015.
1774−1782.
[20] Feng C, Qian C, Tang K, et al. Unsupervised feature selection by Pareto optimization. In: Proc. of the National Conf on Artificial
Intelligence. 2019.
[21] Derrac J, García S, Herrera F. Ifs-Coco: Instance and feature selection based on cooperative coevolution with nearest neighbor rule.
Pattern Recognition, 2010,43(6):2082−2105.
[22] Hoffmeister F, Back T. Genetic algorithms and evolution strategies—Similarities and differences. In: Proc. of the Parallel Problem
Solving from Nature. 1990. 455−469.
[23] Beyer H, Schwefel H. Evolution strategies—A comprehensive introduction. Natural Computing, 2002,1(1):3−52.
[24] Salimans T, Ho J, Chen X, et al. Evolution strategies as a scalable alternative to reinforcement learning. arXiv: Machine Learning,
2017.
[25] Mnih V, Kavukcuoglu K, Silver D, et al. Human-Level control through deep reinforcement learning. Nature, 2015,518(7540):
529−533.
[26] Gu S, Cheng R, Jin Y, et al. Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft
Computing, 2018,22(3):811−822.
[27] Dua D, Karra Taniskidou E. UCI machine learning repository. Irvine: University of California, School of Information and
Computer Science, 2017. http://archive.ics.uci.edu/ml
[28] Rechenberg I. Evolutionsstrategie optimierung technischer systeme nach prinzipien der biologischen evolution. 1973.