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
收藏
页码：108 / 115
页数：8
相关论文
共 50 条
  • [41] A Comparison of Architectural Varieties in Radial Basis Function Neural Networks
    Efe, Mehmet Oender
    Kasnakoglu, Cosku
    2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8, 2008, : 66 - 71
  • [42] A Radial Basis Function Neural Network Approach to Detect Novelties: Applications on Health Datasets
    Pereira, Cassio M. M.
    de Mello, Rodrigo F.
    JCPC: 2009 JOINT CONFERENCE ON PERVASIVE COMPUTING, 2009, : 143 - 148
  • [43] Relation between generalized radial basis function (GRBF) networks and neural networks
    Miyazaki, Akio
    Yamada, Tsuyoshi
    Electronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi), 1993, 76 (08): : 74 - 84
  • [44] Supervised Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
    Neruda, Roman
    Vidnerova, Petra
    2008 SECOND INTERNATIONAL CONFERENCE ON FUTURE GENERATION COMMUNICATION AND NETWORKING SYMPOSIA, VOLS 1-5, PROCEEDINGS, 2008, : 360 - 363
  • [45] A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function
    Simoes, Alexandre da Silva
    Reali Costa, Anna Helena
    ADVANCES IN ARTIFICIAL INTELLIGENCE - SBIA 2008, PROCEEDINGS, 2008, 5249 : 227 - +
  • [46] New enhanced methods for radial basis function neural networks in function approximation
    Fatemi, M. (mefatemi@gmail.com), Operador Nacional do Sistema Eletrico - ONS; Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Inst. of Elec. and Elec. Eng. Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States):
  • [47] New enhanced methods for radial basis function neural networks in function approximation
    Fatemi, M
    Roopaei, M
    Shabaninia, F
    HIS 2005: 5th International Conference on Hybrid Intelligent Systems, Proceedings, 2005, : 524 - 527
  • [48] Chess Position Evaluation Using Radial Basis Function Neural Networks
    Kagkas, Dimitrios
    Karamichailidou, Despina
    Alexandridis, Alex
    COMPLEXITY, 2023, 2023
  • [49] Parallel Radial Basis Function Neural Networks to Solve the Polynomials Equations
    Altaee, Abed Ali H.
    Hoomod, Haider K.
    Hussein, Khalid Ali
    2016 AL-SADIQ INTERNATIONAL CONFERENCE ON MULTIDISCIPLINARY IN IT AND COMMUNICATION TECHNIQUES SCIENCE AND APPLICATIONS (AIC-MITCSA), 2016,
  • [50] A supervised fuzzy clustering for Radial Basis Function Neural Networks training
    Tagliaferri, R
    Staiano, A
    Scala, D
    JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, 2001, : 1804 - 1809
12345