基于滑动窗口的快速Learn++.NSE

Fast Learn++.NSE Algorithm Based on Sliding Window

Learn++.NSE集成的单个基分类器需根据其在所有历经环境中的分类错误率加权计算投票权重,学习效率有待提高.因此,文中采用滑动窗口技术优化权重的计算过程,提出基于滑动窗口的快速Learn++.NSE算法(SWLearn++.NSE).该算法仅考虑使用单个基分类器近期窗口内的分类准确率计算投票权重,提高集成学习的效率.实验表明,相比Learn++.NSE,在取得同等分类准确率的情况下,文中算法分类学习的效率更高.

The vote weight of each base-classifier in Learn ＋ ＋. NSE depends on all the error rates in the environments experienced, and the classification learning efficiency of the Learn ＋ ＋. NSE needs to be improved. Therefore, a fast Learn ＋ ＋. NSE algorithm based on sliding window （SW-Learn＋ ＋. NSE ） is presented in this paper. The sliding window is utilized to optimize the calculation of the weight. By only using the recent classification error rates of each base-classifier inside the sliding window to compute the vote weight, the SW-Learn＋ ＋. NSE improves the efficiency of ensemble classification learning greatly. The experiment shows that the SW-Learn ＋ ＋. NSE achieves a higher execution efficiency with an equivalent classification accuracy compared to the Learn＋＋. NSE.