A multi-label incremental learning algorithm
Yuping Qin, Yawei Tang, Shuxian Lun, Yandong Guo · 2012
A multi-label incremental learning algorithm based on hyper ellipsoidal is proposed. To every class, the smallest hyper ellipsoidal that surrounds most samples of the class is structured, which can divide the class samples from others. In the process of incremental learning, only are the hyper ellipsoidals that its class exist in new incremental samples structured. The multi-label incremental learning is realized in a small memory space, and the history results that has nothing to do with the new incremental sample are saved. For the sample to be classified, its classes be confirmed by the hyper ellipsoidals that surrounds it. If there is not hyper ellipsoidal surround the sample, the membership is used to confirmed its class. The experiments are done on Reuters 21578, and the experiment results show that the algorithm has a higher performance on classification speed and classification precision compare with hyper sphere algorithm.