Intuitionistic fuzzy generalized eigenvalue proximal support vector machine

被引:9
作者
Quadir, A. [1 ]
Ganaie, M. A. [2 ]
Tanveer, M. [1 ]
机构
[1] Indian Inst Technol Indore, Dept Math, Indore 453552, India
[2] Indian Inst Technol Ropar, Dept Comp Sci & Engn, Ropar 140001, Punjab, India
关键词
Fuzzy scheme; Intuitionistic fuzzy; Support vector machine; Eigenvalue; Generalized eigenvalue proximal support; vector machines; SVM;
D O I
10.1016/j.neucom.2024.128258
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted widespread attention due to its simple architecture, rapid execution, and commendable performance. GEPSVM gives equal significance to all samples, thereby diminishing its robustness and efficacy when confronted with real-world datasets containing noise and outliers. In order to reduce the impact of noises and outliers, we propose a novel intuitionistic fuzzy generalized eigenvalue proximal support vector machine (IF-GEPSVM). The proposed IF-GEPSVM assigns the intuitionistic fuzzy score to each training sample based on its location and surroundings in the high-dimensional feature space by using a kernel function. The solution of the IF-GEPSVM optimization problem is obtained by solving a generalized eigenvalue problem. Further, we propose an intuitionistic fuzzy improved generalized eigenvalue proximal support vector machine (IF-IGEPSVM) by solving standard eigenvalue decomposition resulting in simpler optimization problems with less computation cost which leads to an efficient intuitionistic fuzzy-based model. We conduct a comprehensive evaluation of the proposed IF-GEPSVM and IF-IGEPSVM models on UCI and KEEL benchmark datasets. Moreover, to evaluate the robustness of the proposed IFGEPSVM and IF-IGEPSVM models, label noise is introduced into some UCI and KEEL datasets. The experimental findings showcase the superior generalization performance of the proposed IF-GEPSVM and IF-IGEPSVM models when compared to the existing baseline models, both with and without label noise. Our experimental results, supported by rigorous statistical analyses, confirm the superior generalization abilities of the proposed IF-GEPSVM and IF-IGEPSVM models over the baseline models. Furthermore, we implement the proposed IFGEPSVM and IF-IGEPSVM models on the USPS recognition dataset, yielding promising results that underscore the models' effectiveness in practical and real-world applications. The source code of the proposed IF-GEPSVM and IF-IGEPSVM models are available at https://github.com/mtanveer1/IF-GEPSVM.
引用
收藏
页数:17
相关论文
共 48 条
[1]  
Alcalá-Fdez J, 2011, J MULT-VALUED LOG S, V17, P255
[2]   INTUITIONISTIC FUZZY-SETS [J].
ATANASSOV, KT .
FUZZY SETS AND SYSTEMS, 1986, 20 (01) :87-96
[3]   FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning [J].
Batuwita, Rukshan ;
Palade, Vasile .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (03) :558-571
[4]   A new fuzzy twin support vector machine for pattern classification [J].
Chen, Su-Gen ;
Wu, Xiao-Jun .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2018, 9 (09) :1553-1564
[5]   SUPPORT-VECTOR NETWORKS [J].
CORTES, C ;
VAPNIK, V .
MACHINE LEARNING, 1995, 20 (03) :273-297
[6]  
Demsar J, 2006, J MACH LEARN RES, V7, P1
[7]  
Dua D., 2017, UCI MACHINE LEARNING
[8]   A comparison of alternative tests of significance for the problem of m rankings [J].
Friedman, M .
ANNALS OF MATHEMATICAL STATISTICS, 1940, 11 :86-92
[9]   EEG signal classification via pinball universum twin support vector machine [J].
Ganaie, M. A. ;
Tanveer, M. ;
Jangir, Jatin .
ANNALS OF OPERATIONS RESEARCH, 2023, 328 (01) :451-492
[10]   Large-Scale Fuzzy Least Squares Twin SVMs for Class Imbalance Learning [J].
Ganaie, M. A. ;
Tanveer, M. ;
Lin, Chin-Teng .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (11) :4815-4827