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基于密度估计的逻辑回归模型

毛毅 陈稳霖 郭宝龙 陈一昕

毛毅, 陈稳霖, 郭宝龙, 陈一昕. 基于密度估计的逻辑回归模型. 自动化学报, 2014, 40(1): 62-72. doi: 10.3724/SP.J.1004.2014.00062
引用本文: 毛毅, 陈稳霖, 郭宝龙, 陈一昕. 基于密度估计的逻辑回归模型. 自动化学报, 2014, 40(1): 62-72. doi: 10.3724/SP.J.1004.2014.00062
MAO Yi, CHEN Wen-Lin, GUO Bao-Long, CHEN Yi-Xin. A Novel Logistic Regression Model Based on Density Estimation. ACTA AUTOMATICA SINICA, 2014, 40(1): 62-72. doi: 10.3724/SP.J.1004.2014.00062
Citation: MAO Yi, CHEN Wen-Lin, GUO Bao-Long, CHEN Yi-Xin. A Novel Logistic Regression Model Based on Density Estimation. ACTA AUTOMATICA SINICA, 2014, 40(1): 62-72. doi: 10.3724/SP.J.1004.2014.00062

基于密度估计的逻辑回归模型

doi: 10.3724/SP.J.1004.2014.00062
基金项目: 

国家自然科学基金(61105066,61201290,61305041,61305040)资助

详细信息
    作者简介:

    毛毅西 安电子科技大学智能控制与图像工程研究所博士研究生. 2008 年获西安电子科技大学测控计量技术与仪器学士学位. 主要研究方向为数据挖掘与机器学习. 本文通信作者.E-mail:olivia.maoy@gmail.com

A Novel Logistic Regression Model Based on Density Estimation

Funds: 

Supported by National Natural Science Foundation of China (61105066, 61201290, 61305041, 61305040)

  • 摘要: 介绍了一种基于密度的逻辑回归(Density-based logistic regression,DLR)分类模型以解决逻辑回归中非线性分类的问题. 其主要思想是根据Nadarays-Watson密度估计将训练数据映射到特定的特征空间,然后组建优化模型优化特征权重以及Nadarays-Watson 密度估计算法的宽度. 其主要优点在于:它不仅优于标准的逻辑回归,而且优于基于径向基函数(Radial basis function,RBF)内核的核逻辑回归(Kernel logistic regression,KLR). 特别是与核逻辑回归分析和支持向量机(Support vector machine,SVM)相比,该方法不仅达到更好的分类精度,而且有更好的时间效率. 该方法的另一个显著优点是,它可以很自然地扩展到数值类型和分类型混合的数据集中. 除此之外,该方法和逻辑回归(Logistic regression,LR)一样,有同样的模型可解释的优点,这恰恰是其他如核逻辑回归分析和支持向量机所不具备的.
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出版历程
  • 收稿日期:  2013-01-16
  • 修回日期:  2013-04-02
  • 刊出日期:  2014-01-20

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