| [1] GUYON I, ELISSEEFF A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 2003, 3: 1157-1182.
[2] BLUM A L, LANGLEY P. Selection of Relevant Features and Examples in Machine Learning. Artificial Intelligence, 1997, 97(1/2): 245-271.
[3] GUYON I, GUNN S R, NIKRAVESH M, et al. Feature Extraction: Foundations and Applications. Berlin, Germany: Springer, 2006.
[4] LIU H, YU L. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans on Knowledge and Data Engineering, 2005, 17(4): 491-502.
[5] ALMUALLIM H, DIETTERICH T G. Learning Boolean Concepts in the Presence of Many Irrelevant Features. Artificial Intelligence, 1994, 69(1/2): 279-305.
[6] DASH M, LIU H. Consistency-Based Search in Feature Selection. Artificial Intelligence, 2003, 151(1/2): 155-176.
[7] YU L, LIU H. Efficient Feature Selection via Analysis of Relevance and Redundancy. Journal of Machine Learning Research, 2004, 5:1205-1224.
[8] BATTITI R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Trans on Neural Networks, 1994, 5(4): 537-550.
[9] KWAK N, CHOI C H. Input Feature Selection by Mutual Information Based on Parzen Window. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1667-1671.
[10] PENG H C, LONG F H, DING C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238.
[11] KOHAVI R, JOHN G H. Wrappers for Feature Subset Selection. Artificial Intelligence, 1997, 97(1/2): 273-324.
[12] SINDHWANI V, RAKSHIT S, DEODHARE D, et al. Feature Selection in MLPs and SVMs Based on Maximum Output Information. IEEE Trans on Neural Networks, 2004, 15(4): 937-948.
[13] YANG J B, SHEN K Q, ONG C J, et al. Feature Selection for MLP Neural Network: The Use of Random Permutation of Probabilistic Outputs. IEEE Trans on Neural Networks, 2009, 20(12): 1911-1922.
[14] QUINLAN J R. Induction of Decision Trees. Machine Learning, 1986, 1(1): 81-106.
[15] SHEN K Q, ONG C J, LI X P, et al. Feature Selection via Sensitivity Analysis of SVM Probabilistic Outputs. Machine Learning, 2008, 70(1): 1-20.
[16] PERKINS S, LACKER K, THEILER J. Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space. Journal of Machine Learning Research, 2003, 3: 1333-1356.
[17] Holland J H. Adaptation in Natural and Artificial Systems. Cambridge, USA: MIT Press, 1992.
[18] YANG J, HONAVAR V G. Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems, 1998, 13(2): 44-49.
[19] STEFANO C D, FONTANELLA F, MARROCCO C, et al. A GA-Based Feature Selection Approach with an Application to Handwritten Character Recognition. Pattern Recognition Letters, 2014, 35(1): 130-141.
[20] KABIR M M, SHAHJAHAN M, MURASE K. A New Local Search Based Hybrid Genetic Algorithm for Feature Selection. Neurocomputing, 2011, 74(17): 2914-2928.
[21] RAYMER M L, PUNCH W F, GOODMAN E D, et al. Dimensionality Reduction Using Genetic Algorithms. IEEE Trans on Evolutionary Computation, 2000, 4(2): 164-171.
[22] TSAI C F, EBERLE W, CHU C Y. Genetic Algorithms in Feature and Instance Selection. Knowledge-Based Systems, 2013, 39: 240-247.
[23] CHEN Z Y, LIN W C, KE S W, et al. Evolutionary Feature and Instance Selection for Traffic Sign Recognition. Computers in Industry, 2015, 74: 201-211.
[24] 苗夺谦,李道国.粗糙集理论、算法与应用.北京:清华大学出版社, 2008.
(MIAO D Q, LI D G. Rough Set Theories, Algorithms and Applications. Beijing, China: Tsinghua University Press, 2008.)
[25] 王宇平.进化计算的理论和方法.北京:科学出版社, 2011.
(WANG Y P. Evolutionary Computation Theory and Method. Beijing, China: Science Press, 2011.)
[26] 周 涛,陆惠玲,张艳宁,等.基于Rough Set的高维特征选择混合遗传算法研究.南京大学学报(自然科学), 2015, 51(4):880-893.
(ZHOU T, LU H L, ZHANG Y N, et al. A New Hybrid Genetic Algorithm for High Dimension Feature Selection Based on Rough Set. Journal of Nanjing University (Nature Sciences), 2015, 51(4): 880-893.)
[27] 黄 炜,黄志华.一种基于遗传算法和SVM的特征选择.计算机技术与发展, 2010, 20(6): 21-24.
(HUANG W, HUANG Z H. Feature Selection Based on Genetic Algorithm and SVM. Computer Technology and Development, 2010, 20(6): 21-24.) |
2016, Vol. 29 
