基于深度信念网络与随机森林的药品鉴别方法

摘要

To solve the problem that the spectral feature of the near infrared spectrum is too high that the traditional shallow feature extraction method is incapable to extract it,an algorithm based on deep belief network and random forests is put forward.This algorithm combines the deep belief network and the random forest,and it makes use of the powerful ability to analyze and extract the high dimensional eigenvector with the deep belief network,and the good classification performance of the random forests method.The deep belief network is taken as the feature extractor and the random forests method as the classifier.The experimental results show that the proposed method is effective compared with other traditional algorithms on three data sets,and the performance of the algorithm is improved with the increase of the original input characteristic number.%为解决近红外光谱药品鉴别中,光谱数据特征维数较高,传统的浅层特征提取方法学习能力不足的问题,提出了基于深度信念网络和随机森林的药品鉴别算法.算法将深度信念网络与随机森林相结合,利用了深度信念网络对高维特征向量强大的分析和提取能力,以及随机森林良好的分类性能,将深度信念网络作为特征提取器,随机森林方法作为分类器.通过用三种近红外光谱数据集进行实验验证,与其它传统算法作对比,证明了改进方法的有效性,且随着原始输入特征数的提升,算法性能越显优越.