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| Improved Method of Maximizing AUC and Its Application to Obstacle Detection |
| HAN Guang,ZHAO Chun-Xia |
| School of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094 |
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Abstract In the obstacle detection, obstacle/non-obstacle samples have characteristics of a large range of overlapping in the feature space and uneven distribution. The traditional training method for the classifier is not competent for dealing with such data. Thus, an improved method of maximizing area under the ROC (AUC) is proposed to train classifier. An alternative function is used as the objective function of optimizing AUC. Meanwhile, the particle swarm optimization is introduced to optimize the AUC objective function, and the particle swarm optimization algorithm is improved by using the Butterworth curves and particles with the low fitness value being mutated. The experimental results show that the proposed method effectively solves the local optimization caused by the gradient descent method. Moreover, the detection performance of the proposed method is improved compared with other existing algorithms, and the algorithm is reliable and efficient.
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Received: 20 July 2009
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2010, Vol. 23 
