基于样本噪声检测的AdaBoost算法改进

针对传统的AdaBoost算法中，存在的噪声样本造成的过拟合问题，提出了一种基于噪声检测的AdaBoost改进算法，本文称为NAdaBoost（nois-detection AdaBoost）. NAdaBoost算法创新点在于针对传统的AdaBoost算法在错误分类的样本中，噪声样本在某些属性上存在很大差异，根据这一特性来确定噪声样本，再重新使用算法对两类样本进行分类，最终达到提高分类准确率的目的. 本文对二分类问题进行实验结果表明，本文提出的算法和传统的AdaBoost算法，以及相关改进的算法相比，有较高的分类准确率.

Improvement of AdaBoost Algorithm Based on Sample Noise Detection

In the traditional AdaBoost algorithm, there are over-fitting problems caused by noise samples. In this paper, an improved AdaBoost algorithm based on noise detection is proposed, called NAdaBoost. According to the traditional AdaBoost algorithm, in the misclassified samples, noise samples vary widely in some attributes. NAdaBoost can, instead, determine the noise samples based on this, and then reuse the algorithm to classify the two types of samples, and ultimately achieve the purpose of improving the accuracy of classification. The experiment on the binary classification shows that the proposed algorithm has a higher classification accuracy compared with the traditional AdaBoost algorithm, as well as relative improvement of algorithms.