基于PCA改进的快速Adaboost算法研究

针对传统的Adaboost算法可能出现在应对较大训练数据集训练时间过长的问题,提出了一种改进的Adaboost算法——PCAdaboost。改进算法利用PCA方法的降维技术,对训练样本特征提取主要成分,去除输入样本特征间的相关性,提高分类精度。同时,从样本阈值搜索角度考虑了特征值等分和特征值空间维数,给出了阈值快速搜索方法。实验结果表明,该算法在UCI数据集上取得较好的效果。

Fast Adaboost algorithm based on improved PCA

In view of the problem of the long training time in dealing with large training dataset in the training process of the traditional Adaboost algorithm, the authors introduced an improved methods to these problem. Improved algorithm using PCA dimension reduction technique, extracts main ingredients for the training sample feature, removes the correlation between the input sample characteristics, and improves the classification accuracy. At the same time, from the angle of sample threshold search takes into consideration the divisions and eigenvalue space dimension, threshold fast search method is presented. Experimental results show that the algorithm to achieve better results on UCI datasets.