|
|
|
| An Ensemble Detection Method of Pipeline Condition Based on Tabu Search |
| WANG Yong-Xiong 1,2,SU Jian-Bo1 |
1.Key Laboratory of System Control and Information Processing of Ministry of Education,Department of Automation,Shanghai Jiao Tong University,Shanghai 200240
2.School of Electronics and Information Engineering,Jinggangshan University,Ji′an 343009 |
|
|
|
|
Abstract To improve the recognition rate of pipe anomaly detection and real-time performance,an ensemble classification method based on Tabu search is proposed which combines semi-supervise K-means clustering and C4.5 decision tree. The cost-sensitive function is introduced in Tabu search to select the most discriminating feature subset and the best ensemble weights. Thus,the classification performance of the minority class in imbalance data is improved. The semi-supervise K-means approach partitions the features of samples into k clusters firstly. Then,a supervised C4.5 decision tree in each K-means cluster is trained to refine the decision boundaries by learning the subgroups within the cluster. The ensemble classification by cascading K-means and C4.5 alleviates the problems of imbalance data and improves the classification accuracy of imbalance data. The final decisions of the K-means and C4.5 methods are integrated based on the weighted sum rule,the nearest-neighbor rule,and the nearest consensus rule respectively. The experimental results show that the proposed system is effective in classifying imbalance data and has high performance in detecting the anomaly of pipeline.
|
|
Received: 03 November 2011
|
|
|
|
|
|
[1] Duran O,Althoefer K,Seneviratne L D. Automated Pipe Defect Detection and Categorization Using Camera/Laser-Based Profiler and Artificial Neural Network. IEEE Trans on Automation Science and Engineering,2007,4(2): 118-126
[2] Tao Xiang,Gong Shaogang.Video Behavior Profiling for Anomaly Detection. IEEE Trans on Pattern Analysis and Machine Intelligence,2008,30(5): 893-908
[3] Gaddam S R,Phoha V,Balagani K S. K-means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-means Clustering and ID3 Decision Tree Learning Methods. IEEE Trans on Knowledge and Data Engineering,2007,19(3): 345-354
[4] Suna Y,Kamela M S,Wong A K C,et al. Cost-Sensitive Boosting for Classification of Imbalanced Data. Pattern Recognition,2007,40(12): 3358-3378
[5] Yang M D,Su T C. Segmenting Ideal Morphologies of Sewer Pipe Defects on CCTV Images for Automated Diagnosis. Expert Systems with Application,2009,36(2): 3562-3573
[6] Japkowicz N,Stephen S. The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis Journal,2002,6(5): 429-450
[7] Tahir M A,Smith J E,Caleb-Solly P. A Novel Feature Selection Based Semi-Supervised Method for Image Classification // Proc of the 6th International Conference on Computer Vision Systems. Santorini,Greece,2008: 484-493
[8] Zhang Hongbing,Sun Guanyu. Tabu Search Algorithm for Feature Selection. Acta Automatica Sinica,1999,25(4): 487-496 (in Chinese)
(张鸿宾,孙广煜.Tabu搜索在特征选择中的应用.自动化学报,1999,25(4): 487-496)
[9] Tahir M A,Bouridane A,Kurugollu F. Simultaneous Feature Selection and Feature Weighting Using Hybrid Tabu Search / K-Nearest Neighbor Classifier. Pattern Recognition Letters. 2007,28(4): 438-446
[10] Theodoridis S,Koutroumbas K. Pattern Recognition. 4th Edition. London,UK: Elsevier,2009
[11] Basu S,Banerjee A,Mooney R. Semi-Supervised Clustering by Seeding // Proc of the 19th International Conference on Machine Learning. Sydney,Australia,2002: 19-26
[12] Gonzalez R C, Woods R E. Digital Image Processing. 2nd Edition. Upper Saddle River,USA: Prentice-Hall,2002
[13] Sonka M,Hlavac V,Boyle R. Image Processing,Analysis,and Machine Vision. 2nd Edition. Pacific Grove,USA: Brooks/Cole,2002
[14] Zaklit J,Wang Yongxiong,Shen Yantao,et al. Quantitatively Characterizing Automotive Interior Surfaces Using an Optical TIR-Based Texture Sensor // Proc of the IEEE International Conference on Robotics and Biomimetics. Guilin,China,2009: 1721-1726
[15] Latif-Amet A,Ertüzün A,Eric A. An Efficient Method for Texture Defect Detection: Sub-Band Domain Co-Occurrence Matrices. Image and Vision Computing,2000,18(6/7): 543-553
[16] Ghanem S A,Venkatesh S,West G. Multi-Class Pattern Classification in Imbalanced Data // Proc of the IEEE International Conference on Pattern Recognition. Istanbul,Turkey,2010: 2881-2884 |
|
|
|
2013, Vol. 26 
