Radial Basis Function Neural Networks for Datasets with Missing Values

被引：1

作者：

Paiva Mesquita, Diego P. ^{[1
]}

Gomes, Joao Paulo P. ^{[1
]}

机构：

[1] Univ Fed Ceara, Dept Comp Sci, Fortaleza, Ceara, Brazil

来源：

INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA 2016) | 2017年 / 557卷

关键词：

Neural networks; Missing data; RBF neural networks; DISTANCE ESTIMATION; LEARNING-MACHINE; REGRESSION;

D O I：

10.1007/978-3-319-53480-0_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Radial Basis Function Neural Networks (RBFNN) are among the most popular supervised learning methods and showed significant results in various applications. Despite is applicability, RBFNNs basic formulation can not handle datasets with missing attributes. Aiming to overcome this problem, in this work, the RBFNN is modified to deal with missing data. For that, the expected squared distance approach is used to compute the RBF Kernel. The proposed approach showed promising results when compared to standard missing data strategies.

引用

页码：108 / 115

页数：8

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