一种动态的主动多分类方法

Dynamical Active Multiple Classification Method

在面向大数据问题的应用领域中,由于现实世界的多样性和复杂性,经常会遇到大规模的多类别数据挖掘问题,传统的多分类方法一方面存在着超平面不平衡更新的问题,另一方面学习效率较低,对于复杂的多类别数据无法进行高效分类。针对这个问题,本文提出了一种改进的动态主动多分类(Dynamical active multiple classification,DYA)方法,该方法通过将死锁、激活等概念引入到主动多分类过程,在主动多分类过程中随着分类器的不断更新,动态地控制样本是否参与主动学习的过程;同时,采用分位计数、轮换学习方式的主动多分类方法,使得多类别的分类器能够得到平衡的学习和更新。实验结果表明,本文提出的动态主动多分类方法有效提高了模型的学习效率和泛化性能。

In the application of big data theory, there are many large scale multiple classification problems for the diversity and complexity of real world. However, the hyperplane updating of traditional multiple classification methods are not balanced. And the learning efficiency of them are low, and they are not efficient for the complex multiple classification data. To solve this problem, this paper presents an improved dynamical active multiple classification method （DYA）. By combining the definitions of deadlock and activation with the active multiple classification process, the proposed method controls dynamically the status whether the sample is to be involved in the active learning process with the updating of classifier in it. Meanwhile, the active learning method with sub-bit counter and rotation learning approach is used to the balance learning and updating of classifier. The experiment results demonstrate that the proposed DYA method can improve both the learning efficiency and generalization performance.