在线序列主动学习方法

现实世界中存在着大量无类标的数据,如各种医疗图像数据、网页数据等。在大数据时代,这种情况更加突出。标注这些无类标的数据需要付出巨大的代价。主动学习是解决这一问题的有效手段,也是近几年机器学习和数据挖掘领域中的一个研究热点。提出了一种基于在线序列极限学习机的主动学习算法,该算法利用在线序列极限学习机增量学习的特点,可显著提高学习系统的效率。另外,该算法用样例熵作为启发式度量无类标样例的重要性,用K-近邻分类器作为Oracle标注选出的无类标样例的类别。实验结果显示,提出的算法具有学习速度快、标注准确的特点。

Online Sequential Active Learning Approach

In the real world,there are a lot of unlabelled data,such as various medical images and web data,etc.In the era of big data,this situation is more prominent.It is expensive to label large amount of unlabelled data.Active learning is an effective method to solve this problem,and it is one of the hot research topics in the field of machine learning and data mining.Based on online sequential extreme learning machine,an active learning algorithm was proposed in this paper.Due to the nature of incremental learning embedded in online sequential extreme learning machine,the proposed algorithm can significantly improve the efficiency of learning system.Furthermore,the proposed algorithm uses instance entropy as heuristic to measure the importance of the unlabeled instances,and uses K-nearest neighbor classifier as Oracle to label the selected instances.The experimental results show that the proposed algorithm has fast learning speed with exact labeling.