AUC maximization linear classifier based on active learning and its application

Aiming at labeling and ranking difficulties caused by a large number of samples, as well as uneven distribution of samples in outdoor obstacle detection of the autonomous mobile robot, an AUC maximization linear classifier method based on active learning is proposed in this paper. This method firstly uses dynamic clustering algorithm to select the representative samples and labels these samples, then these labeled samples are put in the training set. Next, a linear classifier is trained using the AUC maximization method on the training set. The above process will be repeated until the AUC converges. The experiments are performed on real outdoor environment image database. The experiment results show that the very good detection results are obtained using the method proposed in this paper with only 120 samples. More importantly, using the proposed method can significantly reduce the workload of labeling the samples and size of the sample set, and AUC maximization proposed also excels the existing methods.