自适应量子行为粒子群算法及其在图像分类中的应用研究
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摘要
随着多媒体技术和计算机网络的发展,大规模的数字图像资源和数据涌入互联网,它们和人们的日常生活息息相关。关于如何有效地组织、管理和利用丰富的图像资源的研究,对于未来的数字服务具有重要的理论意义和应用价值。图像分类旨在将原本无序分布的大批量的图像数据进行有序地归类。融合机器学习、人工智能等学科的先进思想和理论,寻找一种快速而有效的图像分类方法已成为当前计算机视觉、模式识别和图像处理等领域研究的一个热点。
本文通过对量子行为粒子群算法以及图像分类的现状进行研究,主要做了以下相关工作：
首先,本文提出了一种自适应量子行为粒子群算法,它基于量子行为粒子群算法,根据进化过程中的群体多样性程度来自适应地调整进化策略,保持群体的活力,在一定程度上克服了量子行为粒子群算法陷入局部最优值的缺陷。通过在标准函数集上进行测试,取得了良好的效果。
其次,本文提出了一种基于自适应量子行为粒子群算法和支持向量机的混合图像分类算法,将自适应量子行为粒子群算法用于同步优化图像特征子集和参数选择,解决了训练支持向量机分类器时必须同时优化特征子集和参数设置的问题。本文研究的目的是在不降低支持向量机分类器性能的前提下,尽可能地选择最小的有用的图像特征子集和合适的参数。通过在真实图像数据集上测试,实验结果表明,本文提出的混合图像分类算法具有良好的性能,是一种有效的方法。
With the development of multimedia technology and computer network, massive digital images appear on the Internet, and they are closely related to our daily life. Researches on how to organize, manage and utilize those rich image resources have important theoretical value and practical significance for future digital service. The goal of image classification is to categorize a large number of digital images into a certain class automatically. Integrating other subjects'advanced theories into image classification, like machine learning, artificial intelligence, etc., to find an efficient method for image classification is still a crucial issue in computer vision, pattern recognition and other research areas.
Based on the studies on Quantum-behaved particle swarm optimization (QPSO) and current situation of image classification, the contributions of this paper are as follows:
First, this paper proposes an adaptive Quantum-behaved particle swarm optimization (AQPSO) based on QPSO. It evaluates the diversity of the swarm and changes evolutionary operators adaptively. It can prevent premature convergence to some extent and outperforms Quantum-behaved particle swarm optimization on standard functions.
Second, this paper proposes a hybrid image classification algorithm based on AQPSO and support vector machine (SVM), which introduces AQPSO to optimize feature subset and parameter estimation for SVM at the same time, aiming to solving the problem of optimizing feature subset and parameter estimation simultaneously while training SVM classifier. The research's objective is to select the minimal feature subset without degrading the performance of SVM. Then, the paper tests the hybrid algorithm on real world image datasets. The experiment results show the hybrid algorithm proposed by this paper performs well and is effective.
本文通过对量子行为粒子群算法以及图像分类的现状进行研究,主要做了以下相关工作：
首先,本文提出了一种自适应量子行为粒子群算法,它基于量子行为粒子群算法,根据进化过程中的群体多样性程度来自适应地调整进化策略,保持群体的活力,在一定程度上克服了量子行为粒子群算法陷入局部最优值的缺陷。通过在标准函数集上进行测试,取得了良好的效果。
其次,本文提出了一种基于自适应量子行为粒子群算法和支持向量机的混合图像分类算法,将自适应量子行为粒子群算法用于同步优化图像特征子集和参数选择,解决了训练支持向量机分类器时必须同时优化特征子集和参数设置的问题。本文研究的目的是在不降低支持向量机分类器性能的前提下,尽可能地选择最小的有用的图像特征子集和合适的参数。通过在真实图像数据集上测试,实验结果表明,本文提出的混合图像分类算法具有良好的性能,是一种有效的方法。
With the development of multimedia technology and computer network, massive digital images appear on the Internet, and they are closely related to our daily life. Researches on how to organize, manage and utilize those rich image resources have important theoretical value and practical significance for future digital service. The goal of image classification is to categorize a large number of digital images into a certain class automatically. Integrating other subjects'advanced theories into image classification, like machine learning, artificial intelligence, etc., to find an efficient method for image classification is still a crucial issue in computer vision, pattern recognition and other research areas.
Based on the studies on Quantum-behaved particle swarm optimization (QPSO) and current situation of image classification, the contributions of this paper are as follows:
First, this paper proposes an adaptive Quantum-behaved particle swarm optimization (AQPSO) based on QPSO. It evaluates the diversity of the swarm and changes evolutionary operators adaptively. It can prevent premature convergence to some extent and outperforms Quantum-behaved particle swarm optimization on standard functions.
Second, this paper proposes a hybrid image classification algorithm based on AQPSO and support vector machine (SVM), which introduces AQPSO to optimize feature subset and parameter estimation for SVM at the same time, aiming to solving the problem of optimizing feature subset and parameter estimation simultaneously while training SVM classifier. The research's objective is to select the minimal feature subset without degrading the performance of SVM. Then, the paper tests the hybrid algorithm on real world image datasets. The experiment results show the hybrid algorithm proposed by this paper performs well and is effective.
引文
[1]I.K. Sethi, I.L. Coman. Mining association rules between low-level image features and high-level concepts [C]. Proceedings of the SPIE Data Mining and Knowledge Discovery,2001, IIIP:279-290.
[2]Vapnik V N. The Nature of Statistical Learning Theory[M], New York, USA:Springer,1995,23-60.
[3]J. Bernardo, A. Smith. Bayesian Theory[M]. New York:Wiley,1994.
[4]Tou J T, Gonzalez R C. Pattern recognition principle[M]. Addison Wesley; Reading,1974.
[5]Mitchell TM. Machine Learning[M]. WCB/McGraw Hill, Boston, MA,1997.
[6]Chapelle O., Haffner P., Vapnik V.N. Support vector machines for histogram-based image classification[C]. IEEE Transactions on Neural Networks, 1999,1055-1064.
[7]Foody, G.M. Mathur, A. A relative evaluation of multiclass image classification by support vector machines[C]. IEEE Transactions on Geoscience and Remote Sensing June,2004,42(6):1335-1343.
[8]Jianming Li, Shuguang Huang, Rongsheng He, Kunming Qian. Image Classification Based on Fuzzy Support Vector Machine[C]. International Symposium on Computational Intelligence and Design,2008,68-71.
[9]Ganapathiraju A., Hamaker J.E., Picone J.. Applications of support vector machines to speech recognition[C]. IEEE Transactions on Signal Processing, 2004,24(8):2348-2355.
[10]Larry M. Manevitz, Malik Yousef. One-class svms for document classification[J]. The Journal of Machine Learning Research,2002,2.
[11]M. Bryant, F. Garber. SVM classifier applied to the MSTAR public data set[C]. Algorithms for Synthetic Aperture Radar Imagery, Proceedings of the SPIE,3721,1999,355-360.
[12]Keerthi S. S., Lin C.-J. Asymptotic behaviors of support vector machines with gaussian kernel[J]. Neural Computation,2003, (15):1667-1689.
[13]J. Neumann, C. Schnorr, G. Steidl. Combined SVM-based feature selection and classification[J]. Mach. Learn,2005, (61):129-150.
[14]Fro hlich H., Chapelle O. Feature selection for support vector machines by means of genetic algorithms [C]. Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento,2003,142-148.
[15]Ding Sheng, Li Shunxin. PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification [C]. International Conference on Information Science and Engineering,2009,4066-4069.
[16]Luo Zhiyong, Zhang Wenfeng, Li Yuxia, Xiang, Min. SVM Parameters Tuning with Quantum Particles Swarm Optimization [C]. International Conference on Cybernetics and Intelligent Systems,2008,324-329.
[17]Cheng-Lung Huang, Chieh-Jen Wang. A GA-based feature selection and parameters optimization for support vector machines [J]. Expert Systems with Applications,2006,31(2):231-240.
[18]Huang C.-L., Chen M.-C., Wang C.-J. Credit scoring with a data mining approach based on support vector machines[J]. Expert Systems with Applications,2006,33(4):847-856.
[19]Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen, Zne-Jung Lee. Particle swarm optimization for parameter determination and feature selection of support vector machines[J]. Expert Systems with Applications,2008,35(4):1817-1824.
[20]Cheng-Lung Huang, Jian-Fan Dun. A distributed PSO-SVM hybrid system with feature selection and parameter optimization [J]. Applied Soft Computing,2008, 8(4):1381-1391.
[21]Gao Hengzhen, Mandal Mrinal K., Wan Jianwei. Classification of Hyperspectral Image with Feature Selection and Parameter Estimation[C]. International Conference on Measuring Technology and Mechatronics Automation,2010, 783-786.
[22]Gao Jin, Peng Jin-Ye, Li Zhan. Application of Improved PSO-SVM Approach in Image Classification[C].2010 Symposium on Photonics and Optoelectronic, 2010,1-4.
[23]Schweizer, W. Numerical quantum dynamics[M]. MA, USA:Hingham,2001.
[24]L.S. Coelho. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications,2010,37(2):1676-1683.
[25]Sun Jun, Feng Bin, Xu Wenbo. Particle swarm optimization with particles having quantum behavior[C]. Proceedings of the 2004 Congress on Evolutionary Computation,2004,325-331.
[26]J. Sun, B. Feng, W. Xu. A global search strategy of quantum-behaved particle swarm optimization [C]. IEEE Conference on Cybernetics and Intelligent Systems,2004,111-116.
[27]J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[C]. International Conference on Neural Networks,1995,1942-1948.
[28]S.N. Omkara, Rahul Khandelwala, T.V.S. Ananthb, G. Narayana Naika, S. Gopalakrishnana. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures [J]. Expert Systems with Applications,2009,36(8):11312-11322.
[29]J. Riget, J. S. Vesterstrom. A diversity-guided particle swarm optimizer—the ARPSO[R]. Technical Report 2002-02, Department of Computer Science, University of Aarhus,2002.
[30]James Kennedy, Russell C. Eberhart, Yuhui Shi. Swarm intelligence[M]. San Francisco:Morgan Kanfimnn,2001.
[31]Chung-Jui Tu, Li-Yeh Chuang, Jun-Yang Chang, Cheng-Hong Yang. Feature Selection using PSO-SVM[J]. IAENG International Journal of Computer Science,2007,111-116.
[32]Dorigo M., Di Caro G. The ant colony optimization meta-heuristic[J]. In D.Come, M. Dorigo,& F. Glover (Eds.), New ideas in optimization, NY, USA: McGraw-Hill,1999,11-32.
[33]Angeline P. J.. Using selection to improve particle swarm optimization[C]. IEEE World Congresson Computational Intelligence,1998,84-89.
[34]M. Pant, T. Radha. A New Particle Swarm Optimization with Quadratic Crossover[C]. Int. Conf. on Advanced Computing and Communications (ADCOM'07), India, IEEE Computer Society Press,2007,81-86.
[35]D.B. Chen, C.X. Zhao. Particle swarm optimization with adaptive population size and its application[J]. Applied Soft Computing,2009,39-48.
[36]Kennedy J., R. C. Eberhart. A discrete binary version of the particle swarm algorithm[C]. Proceedings of the IEEE Conference on Systems, Man, and Cybernetics,1997. New York, NY, USA:Piscataway, NJ:IEEE:4104-4109.
[37]Clerc M., Kennedy J. F.. The particle swarm:Explosion, stability and convergence in a multi-dimensional complex space[C]. IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[38]T. Krink, J. Vesterstr(?)m, J. Riget. Particle Swarm Optimization with Spatial Particle Extension[C]. Proceedings of the Congress on Evolutionary Computation 2002,1474-1479.
[39]R. K. Ursem. Diversity-Guided Evolutionary Algorithms [J]. Lecture Notes in Computer Science,2002,2439:462-471.
[40]Leandro dos Santos Coelho. A quantum particle swarm optimizer with chaotic mutation operator[J]. Chaos, Solitons & Fractals,2008,37(5):1409-1418.
[41]程正兴.小波分析算法与应用[M].西安：西安交通大学出版社,1998.
[42]姚敏,黄燕君.模糊决策方法研究[J].系统工程理论与实践,1999,(11)：61-64.
[43]Swain M. J., D. H. Ballard. Color indexing[J]. International Journal of Computer Vision,1991,7(1):11-32.
[44]John R. Smith, Shih-Fu Chang. Automated binary texture feature sets for image retrieval[C]. In Proc. IEEE Int. Conf. Acoust, Speech, and Signal Proc.,1996.
[45]Ma W.-Y., B. S. Manjunath. A comparison of wavelet transform features for texture image annotation[C]. Proceedings of IEEE International Conference on Image Processing (ICIP),1995, Washington DC, USA. II:256-259.
[46]Hu M.-K. Visual pattern recognition by moment invariants[C]. IEEE Transactions Information Theory,1962,8(2):179-187.
[47]Maimon o., Last M. Knowledge Discovery and Data Mining[J]. The Info-Fuzzy Network (IFN) Methodology, Kluwer,2000.
[48]D Koller, M Sahami. Toward Optimal Feature Selection[C]. Proceedings of the 13th Intenational Conference on Machine Learning,1996,284-292.
[49]G H John, R Kohavi, K Pfleger. Irrelevant Features and the Subset Selection Problem [C]. Proc of the 11th Conference on Machine Learning,1994,121-129.
[50]K Kira, LARendell. The Feature Selection Problem:Traditional Methods and a New Algorithm[C]. Proc of 9th NationalConf on AI,1992,129-134.
[51]Manoranjan Dash, Huan Liu. Feature Selection for Classification[J]. Intelligent Data Analysis,1997,1(3):131-156.
[52]Huang Yuan, Shian-Shyong Tseng, Wu Gangshan, Zhang Fuyan. A Two-Phase Feature Selection Method Using Both Filter and Wrapper[C]. Proc of 1999 IEEE Inter Conf on Systems, Man, and Cybernetics.1999,2(c):132-136.
[53]C. Mattiussi, M. Waibel, D. Floreano. Measures of Diversity for Populations and Distances Between Individuals with Highly Reorganizable Genomes Ecolutionary Computation[J]. MIT Press,2004,12(4):495-515.
[54]X.Yao, Y. Liu, G. M. Lin. Evolutionary programming made faster[J]. IEEE Trans. Evol. Comput.,1999,3(2):82-102.
[55]Wang, Keping, Wang, Xiaojie, Zhong, Yixin. A Weighted Feature Support Vector Machines Method for Semantic Image Classification[C].2010 International Conference on Measuring Technology and Mechatronics Automation,2010, 377-380.
[56]Hongtao Zhang, Hanping Mao. Feature Selection for the Stored-grain Insects Based on PSO and SVM[C].2nd International Workshop on Knowledge Discovery and Data Mining,2009,586-589.
[57]Hsu C. W., Chang C. C., Lin C. J.. A practical guide to support vector classification[OL]. Available at:http://www.csie.ntu.edu.tw/-cjlin/papers/guide/ guide.pdf,2003.
[58]Lin Shih-Wei, Lee Zne-Jung, Chen Shih-Chieh, Tseng Tsung-Yuan. Parameter determination of support vector machine and feature selection using simulated annealing approach[J]. Applied Soft Computing Journal,2008,1505-1512.
[59]Pardo M., Sberveglieri G.. Classification of electronic nose data with support vector machines[J]. Sensors and Actuators B Chemical,2005, (107):730-737.
[60]Chih-Chung Chang, Chih-Jen Lin. LIBSVM:a library for support vector machines[OL]. Software available at http://www.csie.ntu.edu.tw/～cjlin/libsvm, 2001.
[61]易文晟.图像语义检索和分类技术研究[D].博士学位论文,浙江大学,2007.
[2]Vapnik V N. The Nature of Statistical Learning Theory[M], New York, USA:Springer,1995,23-60.
[3]J. Bernardo, A. Smith. Bayesian Theory[M]. New York:Wiley,1994.
[4]Tou J T, Gonzalez R C. Pattern recognition principle[M]. Addison Wesley; Reading,1974.
[5]Mitchell TM. Machine Learning[M]. WCB/McGraw Hill, Boston, MA,1997.
[6]Chapelle O., Haffner P., Vapnik V.N. Support vector machines for histogram-based image classification[C]. IEEE Transactions on Neural Networks, 1999,1055-1064.
[7]Foody, G.M. Mathur, A. A relative evaluation of multiclass image classification by support vector machines[C]. IEEE Transactions on Geoscience and Remote Sensing June,2004,42(6):1335-1343.
[8]Jianming Li, Shuguang Huang, Rongsheng He, Kunming Qian. Image Classification Based on Fuzzy Support Vector Machine[C]. International Symposium on Computational Intelligence and Design,2008,68-71.
[9]Ganapathiraju A., Hamaker J.E., Picone J.. Applications of support vector machines to speech recognition[C]. IEEE Transactions on Signal Processing, 2004,24(8):2348-2355.
[10]Larry M. Manevitz, Malik Yousef. One-class svms for document classification[J]. The Journal of Machine Learning Research,2002,2.
[11]M. Bryant, F. Garber. SVM classifier applied to the MSTAR public data set[C]. Algorithms for Synthetic Aperture Radar Imagery, Proceedings of the SPIE,3721,1999,355-360.
[12]Keerthi S. S., Lin C.-J. Asymptotic behaviors of support vector machines with gaussian kernel[J]. Neural Computation,2003, (15):1667-1689.
[13]J. Neumann, C. Schnorr, G. Steidl. Combined SVM-based feature selection and classification[J]. Mach. Learn,2005, (61):129-150.
[14]Fro hlich H., Chapelle O. Feature selection for support vector machines by means of genetic algorithms [C]. Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento,2003,142-148.
[15]Ding Sheng, Li Shunxin. PSO Parameters Optimization Based Support Vector Machines for Hyperspectral Classification [C]. International Conference on Information Science and Engineering,2009,4066-4069.
[16]Luo Zhiyong, Zhang Wenfeng, Li Yuxia, Xiang, Min. SVM Parameters Tuning with Quantum Particles Swarm Optimization [C]. International Conference on Cybernetics and Intelligent Systems,2008,324-329.
[17]Cheng-Lung Huang, Chieh-Jen Wang. A GA-based feature selection and parameters optimization for support vector machines [J]. Expert Systems with Applications,2006,31(2):231-240.
[18]Huang C.-L., Chen M.-C., Wang C.-J. Credit scoring with a data mining approach based on support vector machines[J]. Expert Systems with Applications,2006,33(4):847-856.
[19]Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen, Zne-Jung Lee. Particle swarm optimization for parameter determination and feature selection of support vector machines[J]. Expert Systems with Applications,2008,35(4):1817-1824.
[20]Cheng-Lung Huang, Jian-Fan Dun. A distributed PSO-SVM hybrid system with feature selection and parameter optimization [J]. Applied Soft Computing,2008, 8(4):1381-1391.
[21]Gao Hengzhen, Mandal Mrinal K., Wan Jianwei. Classification of Hyperspectral Image with Feature Selection and Parameter Estimation[C]. International Conference on Measuring Technology and Mechatronics Automation,2010, 783-786.
[22]Gao Jin, Peng Jin-Ye, Li Zhan. Application of Improved PSO-SVM Approach in Image Classification[C].2010 Symposium on Photonics and Optoelectronic, 2010,1-4.
[23]Schweizer, W. Numerical quantum dynamics[M]. MA, USA:Hingham,2001.
[24]L.S. Coelho. Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems[J]. Expert Systems with Applications,2010,37(2):1676-1683.
[25]Sun Jun, Feng Bin, Xu Wenbo. Particle swarm optimization with particles having quantum behavior[C]. Proceedings of the 2004 Congress on Evolutionary Computation,2004,325-331.
[26]J. Sun, B. Feng, W. Xu. A global search strategy of quantum-behaved particle swarm optimization [C]. IEEE Conference on Cybernetics and Intelligent Systems,2004,111-116.
[27]J. Kennedy, R. C. Eberhart. Particle Swarm Optimization[C]. International Conference on Neural Networks,1995,1942-1948.
[28]S.N. Omkara, Rahul Khandelwala, T.V.S. Ananthb, G. Narayana Naika, S. Gopalakrishnana. Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures [J]. Expert Systems with Applications,2009,36(8):11312-11322.
[29]J. Riget, J. S. Vesterstrom. A diversity-guided particle swarm optimizer—the ARPSO[R]. Technical Report 2002-02, Department of Computer Science, University of Aarhus,2002.
[30]James Kennedy, Russell C. Eberhart, Yuhui Shi. Swarm intelligence[M]. San Francisco:Morgan Kanfimnn,2001.
[31]Chung-Jui Tu, Li-Yeh Chuang, Jun-Yang Chang, Cheng-Hong Yang. Feature Selection using PSO-SVM[J]. IAENG International Journal of Computer Science,2007,111-116.
[32]Dorigo M., Di Caro G. The ant colony optimization meta-heuristic[J]. In D.Come, M. Dorigo,& F. Glover (Eds.), New ideas in optimization, NY, USA: McGraw-Hill,1999,11-32.
[33]Angeline P. J.. Using selection to improve particle swarm optimization[C]. IEEE World Congresson Computational Intelligence,1998,84-89.
[34]M. Pant, T. Radha. A New Particle Swarm Optimization with Quadratic Crossover[C]. Int. Conf. on Advanced Computing and Communications (ADCOM'07), India, IEEE Computer Society Press,2007,81-86.
[35]D.B. Chen, C.X. Zhao. Particle swarm optimization with adaptive population size and its application[J]. Applied Soft Computing,2009,39-48.
[36]Kennedy J., R. C. Eberhart. A discrete binary version of the particle swarm algorithm[C]. Proceedings of the IEEE Conference on Systems, Man, and Cybernetics,1997. New York, NY, USA:Piscataway, NJ:IEEE:4104-4109.
[37]Clerc M., Kennedy J. F.. The particle swarm:Explosion, stability and convergence in a multi-dimensional complex space[C]. IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[38]T. Krink, J. Vesterstr(?)m, J. Riget. Particle Swarm Optimization with Spatial Particle Extension[C]. Proceedings of the Congress on Evolutionary Computation 2002,1474-1479.
[39]R. K. Ursem. Diversity-Guided Evolutionary Algorithms [J]. Lecture Notes in Computer Science,2002,2439:462-471.
[40]Leandro dos Santos Coelho. A quantum particle swarm optimizer with chaotic mutation operator[J]. Chaos, Solitons & Fractals,2008,37(5):1409-1418.
[41]程正兴.小波分析算法与应用[M].西安：西安交通大学出版社,1998.
[42]姚敏,黄燕君.模糊决策方法研究[J].系统工程理论与实践,1999,(11)：61-64.
[43]Swain M. J., D. H. Ballard. Color indexing[J]. International Journal of Computer Vision,1991,7(1):11-32.
[44]John R. Smith, Shih-Fu Chang. Automated binary texture feature sets for image retrieval[C]. In Proc. IEEE Int. Conf. Acoust, Speech, and Signal Proc.,1996.
[45]Ma W.-Y., B. S. Manjunath. A comparison of wavelet transform features for texture image annotation[C]. Proceedings of IEEE International Conference on Image Processing (ICIP),1995, Washington DC, USA. II:256-259.
[46]Hu M.-K. Visual pattern recognition by moment invariants[C]. IEEE Transactions Information Theory,1962,8(2):179-187.
[47]Maimon o., Last M. Knowledge Discovery and Data Mining[J]. The Info-Fuzzy Network (IFN) Methodology, Kluwer,2000.
[48]D Koller, M Sahami. Toward Optimal Feature Selection[C]. Proceedings of the 13th Intenational Conference on Machine Learning,1996,284-292.
[49]G H John, R Kohavi, K Pfleger. Irrelevant Features and the Subset Selection Problem [C]. Proc of the 11th Conference on Machine Learning,1994,121-129.
[50]K Kira, LARendell. The Feature Selection Problem:Traditional Methods and a New Algorithm[C]. Proc of 9th NationalConf on AI,1992,129-134.
[51]Manoranjan Dash, Huan Liu. Feature Selection for Classification[J]. Intelligent Data Analysis,1997,1(3):131-156.
[52]Huang Yuan, Shian-Shyong Tseng, Wu Gangshan, Zhang Fuyan. A Two-Phase Feature Selection Method Using Both Filter and Wrapper[C]. Proc of 1999 IEEE Inter Conf on Systems, Man, and Cybernetics.1999,2(c):132-136.
[53]C. Mattiussi, M. Waibel, D. Floreano. Measures of Diversity for Populations and Distances Between Individuals with Highly Reorganizable Genomes Ecolutionary Computation[J]. MIT Press,2004,12(4):495-515.
[54]X.Yao, Y. Liu, G. M. Lin. Evolutionary programming made faster[J]. IEEE Trans. Evol. Comput.,1999,3(2):82-102.
[55]Wang, Keping, Wang, Xiaojie, Zhong, Yixin. A Weighted Feature Support Vector Machines Method for Semantic Image Classification[C].2010 International Conference on Measuring Technology and Mechatronics Automation,2010, 377-380.
[56]Hongtao Zhang, Hanping Mao. Feature Selection for the Stored-grain Insects Based on PSO and SVM[C].2nd International Workshop on Knowledge Discovery and Data Mining,2009,586-589.
[57]Hsu C. W., Chang C. C., Lin C. J.. A practical guide to support vector classification[OL]. Available at:http://www.csie.ntu.edu.tw/-cjlin/papers/guide/ guide.pdf,2003.
[58]Lin Shih-Wei, Lee Zne-Jung, Chen Shih-Chieh, Tseng Tsung-Yuan. Parameter determination of support vector machine and feature selection using simulated annealing approach[J]. Applied Soft Computing Journal,2008,1505-1512.
[59]Pardo M., Sberveglieri G.. Classification of electronic nose data with support vector machines[J]. Sensors and Actuators B Chemical,2005, (107):730-737.
[60]Chih-Chung Chang, Chih-Jen Lin. LIBSVM:a library for support vector machines[OL]. Software available at http://www.csie.ntu.edu.tw/～cjlin/libsvm, 2001.
[61]易文晟.图像语义检索和分类技术研究[D].博士学位论文,浙江大学,2007.