A New Neural Network to Process Missing Data without Imputation

被引：4
|
作者：
Randolph-Gips, M. [1 ]
机构：
[1] Univ Houston Clear Lake, Houston, TX 77058 USA
来源：
SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ICMLA.2008.89
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces the Cosine Neural Network (COSNN) and shows how it can be used to process data with missing components without imputation. It uses a cosine basis function with a weighted norm which can be trained to match the input data, or it can be set to zero to 'ignore' missing data components. The COSNN is compared to Feedforward Neural Networks using deletion and imputation. The COSNN is shown to be superior in both a function approximation and a classification test set.
引用
收藏
页码：756 / 762
页数：7
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