Please wait a minute...
浙江大学学报(工学版)
J4  2013, Vol. 47 Issue (1): 15-22    DOI: 10.3785/j.issn.1008-973X.2013.01.003
计算机技术﹑电信技术     
基于支持向量机的多传感器探测目标分类方法
李侃1, 黄文雄1, 黄忠华2
1.北京理工大学 计算机学院,北京100081；2.北京理工大学 机电学院,北京100081
Multi-sensor detected object classification method based on
support vector machine
LI Kan1, HUANG Wen-xiong1, HUANG Zhong-hua2
1. School of Computer, Beijing Institute of Technology, Beijing 100081, China;2. School of Mechano-Electronics
Engineering, Beijing Institute of Technology, Beijing 100081, China
 全文: PDF  HTML
摘要：

针对传感器探测的数据常含有噪声,分类算法易受噪声数据干扰、容错能力差而产生错分问题,研究对多传感器探测目标进行分类的方法.提出容噪最小二乘投影双支持向量机(NLSPTSVM),去除离群点,提高容噪性能；通过定义NLSPTSVM置信度,以样本的最小超球体距为依据,根据“越是上层分类器的分类性能对分类模型的推广性能影响越大”的思想,以置信度NLSPTSVM作为二分类器,将NLSPTSVM的降噪过程提前到生成有向图之前,提出分类精度高、容噪性和容错性强的多分类支持向量机——容噪上层择优多路支持向量机(NUMDAG-SVMs).实验表明,NUMDAG-SVMs与同类算法相比具有更优的分类准确率和更强的容噪性和容错性.采用NUMDAG-SVMs对传感器采集的真实数据进行分类,取得了很好的结果.
Abstract:

Multi-sensor detected data often have noise. The current multiple classification algorithms are susceptible to noise interference, have weak fault-tolerance, and can lead to data misclassification. The multi-sensor detected object classification method was proposed in order to solve the problems. Noise-tolerance least squares projection twin support vector machine (NLSPTSVM) was presented in order to remove outliers to improve noise-tolerance.  NLSPTSVM with confidence-degree,based on the defined confidence-degree of NLSPTSVM and the minimal hypersphere distance, was used as binary classifier, and advanced the noise reduction process  before the generation of directed graph, according to the idea that “the upper classification performance has more effects on the  generalization performance of classification model”. A high accuracy,  noise-tolerance and fault-tolerance multiple classification support vector machine was proposed,  called  noise-tolerance up-preferred multiple directed acyclic graph support vector machines (NUMDAG-SVMs). Experiments were conducted to test the performance of the algorithm. Experimental results in public datasets indicate that our NUMDAG-SVMs have comparable classification accuracy, better noise-tolerance and fault-tolerance to those other algorithms. The algorithm can get good classification performance in sensor data．
出版日期: 2013-01-01
:  TP 181  
基金资助:

国家自然科学基金资助项目(60903071)
作者简介: 李侃(1975-),男,教授,博导,从事机器学习与数据挖掘的研究.E-mail:likan@bit.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　

引用本文:

李侃,黄文雄,黄忠华. 基于支持向量机的多传感器探测目标分类方法[J]. J4, 2013, 47(1): 15-22.

LI Kan, HUANG Wen-xiong, HUANG Zhong-hua. Multi-sensor detected object classification method based on
support vector machine. J4, 2013, 47(1): 15-22.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2013.01.003    或    http://www.zjujournals.com/eng/CN/Y2013/V47/I1/15

［1］ TRAN D A, NGUYEN T. Localization in wireless sensor network based on support vector machines ［J］. IEEE Transactions on Parallel and Distributed Systems, 2008, 19(7): 981-994．
［2］ GHASEMZADEH H, LOSEU V, JAFARI R. Structural action recognition in body sensor networks: distributed classification based on string matching ［J］. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(2): 425-435．
［3］ BROOKS R R. Distributed target classification and tracking in sensor networks ［J］. Proceedings of the IEEE, 2003, 91(8): 1163-1171．
［4］ MARCO F D, YU H H. Vehicle classification in distributed sensor networks ［J］. Journal of Parallel and Distributed Computing, 2004, 64(7): 826-838.
［5］ COMMAULT C, DION J M, TRINH D H, et al. Sensor classification for the fault detection and isolation, a structural approach ［J］. International Journal of Adaptive Control and Signal Processing, 2011, 25(1): 1-17．
［6］ BINGLEI G, JUNGHO I, GIORGOS M. An artificial immune network approach to multi-sensor land use/land cover classification ［J］. Remote Sensing of Environment, 2011, 115(2): 600-614．
［7］ LIU L. A binary-classification-tree based framework for distributed target classification in multimedia sensor networks ［C］∥ International Conference on Computer Communications. Orlando: ［s. n.］, 2012: 594-602．
［8］ GRUBER C, GRUBER T, KRINNINGER S, et al. Online signature verification with support vector machines based on LCSS kernel functions ［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2010, 40(4): 1088-1100．
［9］ KOTSIA I, PITAS I. Facial expression recognition in image sequences using geometric deformation features and support vector machines ［J］. IEEE Transactions on Image Processing, 2007, 16(1): 172-187．
［10］ SOLERAURENA R, GARCIAMORAL A I. Real-time robust automatic speech recognition using compact support vector machines ［J］. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(4): 1347-1361．
［11］ LORENA A C, DECARVALHO C P L F, GAMA J M P. A review on the combination of binary classifiers in multiclass problems ［J］. Artificial Intelligence Review, 2008, 30(1/2/3/4): 19-37．
［12］ PLATT J C, CRISTIANINI N, SHAWETAYLOR J. Large margin DAGs for multiclass classification ［C］∥ Advances in Neural Information Processing Systems. Cambridge: MIT, 2000: 547-553．
［13］ ANGIULLI F. Fast nearest neighbor condensation for large data sets classification ［J］. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(11): 1450-1464．
［14］ ISA D, LEE L H, KALLMANI V P, et al. Text document preprocessing with the bayesformula for classification using the support vector machine ［J］. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(9): 1264-1272．
［15］ FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained partbased models ［J］. IEEE Transactions on Knowledge and Data Engineering, 2010, 32(9): 1627-1645．
［16］ YUAN H S, NAI Y D, ZHI M Y. Least squares recursive projection twin support vector machine ［J］. Pattern Recognition, 2012, 45(6): 2299-2307．
［17］ STONE M. Cross-validatory choice and assessment of statistical predictions ［J］. Journal of the Royal Statistical Society B, 1974, 36 (1): 111-147．
[1] 林亦宁, 韦巍, 戴渊明. 半监督Hough Forest跟踪算法[J]. J4, 2013, 47(6): 977-983.
[2] 王洪波, 赵光宙, 齐冬莲, 卢达. 一类支持向量机的快速增量学习方法[J]. J4, 2012, 46(7): 1327-1332.
[3] 潘俊, 孔繁胜, 王瑞琴. 局部敏感判别直推学习机[J]. J4, 2012, 46(6): 987-994.
[4] 艾解清, 高济, 彭艳斌, 郑志军. 基于直推式支持向量机的协商决策模型[J]. J4, 2012, 46(6): 967-973.
[5] 金卓军, 钱徽, 朱淼良. 基于倾向性分析的轨迹评测技术[J]. J4, 2011, 45(10): 1732-1737.
[6] 顾弘, 赵光宙. 广义局部图像距离函数下的图像分类与识别[J]. J4, 2011, 45(4): 596-601.
[7] 罗建宏,陈德钊. 兼顾正确率和差异性的自适应集成算法及应用[J]. J4, 2011, 45(3): 557-562.
[8] 商秀芹, 卢建刚, 孙优贤. 基于遗传规划的铁矿烧结终点2级预测模型[J]. J4, 2010, 44(7): 1266-1269.
[9] 徐磊, 赵光宙, 顾弘. 成对耦合分类器的多球体预处理方法[J]. J4, 2010, 44(2): 237-242.