Feature and weight selection using Tabu search for improving the recognition rate of duct anomaly
Wang Yongxiong, Kai Li · 2014
In real-time anomaly detection problems, reducing the dimensionality and improving recognition rate are two most crucial problems. The unbalanced data distribution is one of main reasons of leading to low recognition rate. In this paper, a hybrid approach using Tabu search (TS) and ensemble classification algorithm is proposed. Tabu search is simultaneously applied to select features and weights of ensemble classification. To relieve unbalanced data problems, three policies are used: taking advantages of the cost function of TS to attach more importance to high recognition rate of minority class in the process of feature selection, constructing new sample sets by using oversampling and undersampling, and using ensemble classification method to improve the detection accuracy at low false positive rates. Experimental results show that the approach is effective to improve the classification accuracy of unbalanced class.