支持在线学习的增量式极端随机森林分类器

摘要

This paper proposes an incremental extremely random forest (IERF) algorithm, dealing with online learning classification with streaming data, especially with small streaming data. In this method, newly arrived examples are stored at the leaf nodes and used to determine when to split the leaf nodes combined with Gini index, so the trees can be expanded efficiently and fast with a few examples. The proposed online IERF algorithm gives more competitive or even better performance, than the offline extremely random forest (ERF) method, based on the UCI data experiment. On the moderate training datasets, the IERF algorithm beats the decision tree reconstruction algorithm and other incremental learning algorithms on the performance. Finally, the IERF algorithm is used to solve online video object tracking (multi-object tracking also included) problems, and the results on the challenging video sequences demonstrate its effectiveness and robustness.%提出了一种增量式极端随机森林分类器(incremental extremely random forest,简称IERF),用于处理数据流,特别是小样本数据流的在线学习问题.IERF算法中新到达的样本将被存储到相应的叶节点,并通过Gini系数来确定是否对当前叶节点进行分裂扩展,在给定有限数量,甚至是少量样本的情况下,IERF算法能够快速高效地完成分类器的增量构造.UCI数据集的实验证明,提出的IERF算法具有与离线批量学习的极端随机森林(extremely random forest,简称ERF)算法相当甚至更优的性能,在适度规模的样本集上,性能优于贪婪决策树重构算法和其他几种主要的增量学习算法.最后,提出的IERF算法被应用于解决视频在线跟踪(包含多目标跟踪)问题,基于多个真实视频数据的实验充分验证了算法的有效性和稳定性.