Class Imbalance Learning Using Fuzzy SVM with Fuzzy Weighted Gaussian Kernel

被引：0
|
作者：
Janasruthi [1 ]
Katiyar, Kuldip [1 ]
机构：
[1] Chandigarh Univ, Dept Math, Mohali, Punjab, India
来源：
ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2023, PT IV | 2024年 / 2093卷
关键词：
SVM; FSVM; CIL; FSVM-CIL; Gaussian Kernel; FuzzyWeights; SUPPORT VECTOR MACHINES; CLASSIFICATION; SYSTEMS;
D O I：
10.1007/978-3-031-64067-4_12
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Support Vector Machines is a quite common algorithm to work with datasets which are balanced. In case of imbalanced datasets, SVM are resistant to irregularities with regard to dataset. On the other hand, CIL method is used to reduce the irregularities present. Again, fuzzy SVM also plays great role in manipulating the irregularities. In this paper, we imposed the fuzzy weighted kernel in evaluating the membership values and these memberships values are integrated into the objective function of SVM algorithm and develop the model and predict the overall results.
引用
收藏
页码：171 / 186
页数：16
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