Fuzzy c-Regression Models for Fuzzy Numbers on a Graph

被引:0
|
作者
Higuchi, Tatsuya [1 ]
Miyamoto, Sadaaki [1 ]
Endo, Yasunori [1 ]
机构
[1] Univ Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
关键词
graph structure; clustering; c-regression; outlier detection; dimensionality reduction;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the assumption that the vertices have numerical values. The aim of this paper is to construct regression models to estimate the values from their relationship on the graph by defining the vertex and the numerical value as an independent variable and a dependent variable, respectively. Given the condition that near vertices have close values, k-Nearest Neighbor regression models (KNN) has been proposed. However, the condition is not satisfied when some near vertices have different values. To overcome such difficulty, c-regression which classify data points into some clusters has been proposed to improve performance of regression analysis. We moreover propose new c-regression models on a graph with fuzzy numbers on vertices and show some numerical examples.
引用
收藏
页码:521 / 534
页数:14
相关论文
共 50 条
  • [41] Designing Distributed Fuzzy Rule-Based Models
    Cui, Ye
    E, Hanyu
    Pedrycz, Witold
    Li, Zhiwu
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (07) : 2047 - 2053
  • [42] CONSTRUCTION OF FUZZY MODELS THROUGH CLUSTERING-TECHNIQUES
    YOSHINARI, Y
    PEDRYCZ, W
    HIROTA, K
    FUZZY SETS AND SYSTEMS, 1993, 54 (02) : 157 - 165
  • [43] Fuzzy C Strange Points Clustering Algorithm
    Johnson, Terence
    Singh, Santosh Kumar
    2016 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES), 2016,
  • [44] Fuzzy C-Means on Metric Lattice
    Meng, X.
    Liu, M.
    Zhou, H.
    Wu, J.
    Xu, F.
    Wu, Q.
    AUTOMATIC CONTROL AND COMPUTER SCIENCES, 2020, 54 (01) : 30 - 38
  • [45] Intuitive Fuzzy C-Means Algorithm
    Park, Dong-Chul
    2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009), 2009, : 83 - 88
  • [46] A multivariate fuzzy c-means method
    Pimentel, Bruno A.
    de Souza, Renata M. C. R.
    APPLIED SOFT COMPUTING, 2013, 13 (04) : 1592 - 1607
  • [47] Categorical fuzzy entropy c-means
    Mahamadou, Abdoul Jalil Djiberou
    Antoine, Violaine
    Nguifo, Engelbert Mephu
    Moreno, Sylvain
    2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2020,
  • [48] Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes
    Dovzan, Dejan
    Skrjanc, Igor
    ISA TRANSACTIONS, 2011, 50 (02) : 159 - 169
  • [49] Vectorized Kernel-Based Fuzzy C-Means: a Method to Apply KFCM on Crisp and Non-Crisp Numbers
    Hossein-Abad, Hadi Mahdipour
    Shabanian, Mohsen
    Kazerouni, Iman Abaspur
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2020, 28 (04) : 635 - 659
  • [50] Sparsity Fuzzy C-Means Clustering With Principal Component Analysis Embedding
    Chen, Jingwei
    Zhu, Jianyong
    Jiang, Hongyun
    Yang, Hui
    Nie, Feiping
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2023, 31 (07) : 2099 - 2111