一种基于降噪自编码的组合分类算法  

Ensemble Learning Based on Denosing Autoencoder

作　　者：郭华平[1] 刁小宇 刘宏兵 GUO Huaping;DIAO Xiaoyu;LIU Hongbing(College of Computer and Information Technology,Xinyang Normal University,Xinyang 464000,China;Network Information and Calculating Center,Xinyang Normal University,Xinyang 464000,China)

机构地区：[1]信阳师范学院计算机与信息技术学院,河南信阳464000 [2]信阳师范学院网络信息与计算中心,河南信阳464000

出　　处：《信阳师范学院学报（自然科学版）》2020年第4期657-662,共6页Journal of Xinyang Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(31900710);河南省科技计划项目(182102210132);信阳师范学院研究生科研创新基金项目(2018KYJJ51)。

摘　　要：针对传统分类学习算法的准确性现状进行了研究,提出了一种基于降噪自编码的组合分类算法(Ensemble Learning based on Denosing Autoencoder,ELDA).与Bagging、Adaboost以及Rotation Forest等传统的组合分类器学习方法不同,ELDA首先通过使用降噪自编码算法将数据集映射到新的特征空间,然后在此空间学习得到决策树作为基分类器,最后对数据集进行类别预测.通过与Bagging、Adaboost及Rotation Forest学习方法相比,结果表明:ELDA在预测精度上显著优于对比算法.A ensemble learning method(Ensemble Learning based on Denosing Autoencoder,ELDA)is proposed to study the accuracy of traditional classification learning algorithm.Unlike traditional ensemble learning approaches such as Bagging,Adaboost and Rotation Forest,ELDA first maps the data sets to a new feature space by using denoising autoencoder algorithm,then constructs the decision tree as the base classifier,and further classifies the data sets.Experimental results show that the accuracy of ELDA is higher than others,and it is proved that ELDA is an effective classifier ensemble algorithm of denosing autoencoder.

关 键 词：决策树 自编码 降噪自编码 组合学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]