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知识引导微粒群优化特征选择方法

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Keywords: 微粒群优化,特征选择,特征划分,疾病诊断

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Abstract:

特征选择是模式分类中重要的数据处理方法.文中提出一种基于知识引导微粒群优化的特征选择方法.该方法采用特征被选择的概率对微粒进行编码,将包含离散变量的特征选择问题转化为一类连续变量优化问题.依据微粒适应值的大小及微粒分量被选择的频率,确定特征所属的类型及其被更新的概率,以加快微粒群收敛的速度.将所提方法应用于10个典型测试数据集及肝炎病临床诊断数据集,实验结果表明,该方法在减少特征个数的前提下,能够提高分类的精度。

References

[1]  Zhou Yong, He Chuanxin. Bearing Fault Diagnosis under Varying Load Conditions Based on Individual Feature Selection and Relevance Vector Machine. Journal of Vibration and Shock, 2012, 31(3): 157-161 (in Chinese)(周 勇,何创新.基于独立特征选择与相关向量机的变载荷轴承故障诊断.振动与冲击, 2012, 31(3): 157-161)
[2]  Abdi M J, Giveki D. Automatic Detection of Erythemato-Squamous Diseases Using PSO-SVM Based on Association Rules. Engineering Applications of Artificial Intelligence, 2013, 26(1): 603-608
[3]  Xu Lei, Li Yongzhong, Li Zhengjie. Network Intrusion Detection Algorithm Based on Quantum-Behaved Particle Swarm Optimization. Computer Engineering and Applications, 2011, 47(36): 102-104 (in Chinese)(徐磊,李永忠,李正洁.基于量子粒子群优化的网络入侵检测算法.计算机工程与应用, 2011, 47(36): 102-104)
[4]  Wu Guanghua, Liu Guangyuan, Long Zhengji. Effect of Immune Mechanism for Emotion Feature Selection from GSR Signal. Application Research of Computers, 2010, 27(12): 4558-4564 (in Chinese)(吴光华,刘光远,龙正吉.免疫机制对皮肤电信号情感特征选择的影响.计算机应用研究, 2010, 27(12): 4558-4564)
[5]  Liu Yuanning, Wang Gang, Chen Huiling, et al. An Improved Particle Swarm Optimization for Feature Selection. Journal of Bionic Engineering, 2011, 8(2): 191-200
[6]  Chuang L Y, Chang H W, Tu C J, et al. Improved Binary PSO for Feature Selection Using Gene Expression Data. Computational Biology and Chemistry, 2008, 32(1): 29-38
[7]  Falco I D, Cioppa A D, Tarantino E. Facing Classification Problems with Particle Swarm Optimization. Applied Soft Computing, 2007, 7(3): 652-658
[8]  Kennedy J, Eberhart R C. A Discrete Binary Version of the Particle Swarm Algotithm // Proc of the IEEE International Conference on Systems, Man, and Cybernetics. Orlando, USA, 1997, V: 4104 -4108
[9]  Peng Sihua, Xu Qianghua, Ling X B, et al. Molecular Classification of Cancer Types from Microarray Data Using the Combination of Genetic Algorithms and Support Vector Machines. Febs Letters, 2003, 555(2): 358-362
[10]  Wang Xiangyang, Yang Jie, Teng Xiaolong, et al. Feature Selection Based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, 2007, 28(4): 459-471
[11]  Yao Quanzhu, Cai jie. Feature Selection and LS-SVM Parameter Optimization Algorithm Based on the PSO. Computer Engineering and Applications, 2010, 46(1): 134-136 (in Chinese)(姚全珠,蔡婕.基于PSO的LS-SVM特征选择与参数优化算法.计算机工程与应用, 2010, 46(1): 134-136)
[12]  Guo Wenzhong, Chen Guolong, Chen Qingliang. Feature Subset Selection Based on Particle Swarm Optimization Algorithm and Correlation Analysis. Computer Science, 2008, 35(2): 144-146 (in Chinese)(郭文忠,陈国龙,陈庆良.基于粒子群优化算法和相关性分析的特征子集选择.计算机科学, 2008, 35(2): 144-146)
[13]  Unler A, Murat A. A Discrete Particle Swarm Optimization Method for Feature Selection in Binary Classification Problems. European Journal of Operational Research, 2010, 206(3): 528-539
[14]  Chuang L Y, Yang C H, Li J C. Chaotic Maps Based on Binary Particle Swarm Optimization for Feature Selection. Applied Soft Computing, 2011, 11(1): 239-248
[15]  Marinakis Y, Marinaki M. A Hybridized Particle Swarm Optimization with Expanding Neighborhood Topology for the Feature Selection Problem // Proc of the 8th International Workshop on Hybrid Metaheuristics. Ischia, Italy, 2013: 37-51
[16]  Babaoglu I, Findik O, lker E. A Comparison of Feature Selection Models Utilizing Binary Particle Swarm Optimization and Genetic Algorithm in Determining Coronary Artery Disease Using Support Vector Machine. Expert Systems with Applications, 2010, 37(4): 3177-3183
[17]  Vieira S M, Mendonca L F, Farinha G J, et al. Modified Binary PSO for Feature Selection Using SVM Applied to Mortality Prediction of Septic Patients. Applied Soft Computing, 2013, 13(8): 3494-3504
[18]  Sweilam N H, Tharwat A A, Abdel Moniem N K. Support Vector Machine for Diagnosis Cancer Disease: A Comparative Study. Egyptian Informatics Journal, 2010, 11(2): 81-92
[19]  Yu Hualong, Gu Guochang, Liu Haibo, et al. Feature Gene Selection by Combining an Improved Discrete PSO and SVM. Journal of Harbin Engineering University, 2009, 30(12): 1399-1403 (in Chinese)(于化龙,顾国昌,刘海波,等.改进的离散PSO和SVM的特征基因选择算法.哈尔滨工程大学学报, 2009, 30(12): 1399-1403)
[20]  Wang Li, Wang Xiaokai. Modified Particle Swarm Optimizer Using Non-Linear Inertia Weight. Computer Engineering and Applications, 2007, 43(4): 47-48,92 (in Chinese)(王 丽,王晓凯.一种非线性改变惯性权重的粒子群算法.计算机工程与应用, 2007, 43(4): 47-48,92)

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