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

共 50 条

[31] Radial basis function neural networks for reliably forecasting rainfall
El Shafie, Amr H.
El-Shafie, A.
Almukhtar, A.
Taha, Mohd. R.
El Mazoghi, Hasan G.
Shehata, A.
JOURNAL OF WATER AND CLIMATE CHANGE, 2012, 3 (02) : 125 - 138
[32] Analysis of decision boundaries of radial basis function neural networks
Jung, E
Lee, C
ALGORITHMS AND SYSTEMS FOR OPTICAL INFORMATION PROCESSING IV, 2000, 4113 : 134 - 142
[33] A classification technique based on radial basis function neural networks
Sarimveis, H
Doganis, P
Alexandridis, A
ADVANCES IN ENGINEERING SOFTWARE, 2006, 37 (04) : 218 - 221
[34] Random vibration analysis with radial basis function neural networks
Xi Wang
Jun Jiang
Ling Hong
Jian-Qiao Sun
International Journal of Dynamics and Control, 2022, 10 : 1385 - 1394
[35] Approximation of nonlinear systems with radial basis function neural networks
Schilling, RJ
Carroll, JJ
Al-Ajlouni, AF
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2001, 12 (01): : 1 - 15
[36] On the construction and training of reformulated radial basis function neural networks
Karayiannis, NB
Randolph-Gips, MM
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (04): : 835 - 846
[37] Resisting the influence of outliers in radial basis function neural networks
Tsai, JR
Chung, PC
Chang, CI
NEURAL NETWORKS FOR SIGNAL PROCESSING VI, 1996, : 42 - 51
[38] A kernel PCA radial basis function neural networks and application
Li, Qingzhen
Zhao, Jiufen
Zhu, Xiaoping
2006 9TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION, VOLS 1- 5, 2006, : 759 - +
[39] Improving the Generalization Properties of Radial Basis Function Neural Networks
Bishop, Chris
NEURAL COMPUTATION, 1991, 3 (04) : 579 - 588
[40] Descartes' rule of signs for radial basis function neural networks
Schmitt, M
NEURAL COMPUTATION, 2002, 14 (12) : 2997 - 3011

← 1 2 3 4 5 →