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.
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2013, Vol. 26 
