Intuitionistic Fuzzy SVM based on Kernel Gray Relational Analysis

Fuzzy Support Vector Machine (FSVM) is a machine learning algorithm that combines fuzzy logic with Support Vector Machine (SVM) to deal with the uncertainty and fuzziness in classification and regression problems. This algorithm improves the performance of traditional SVM by introducing fuzzy membership degrees, making it more robust when handling datasets with noise or uncertainty. Although the existing FSVM algorithms can overcome the influence of noise to a certain extent, they cannot effectively distinguish outliers or abnormal values from boundary support vectors. To solve this problem, this study proposes an Intuitionistic Fuzzy Support Vector Machine algorithm (KGRA-IFSVM) based on Kernel Grey Relational Analysis (KGRA). This approach utilizes gray relational analysis in the kernel space to calculate the gray relational degree between each sample and its K isomorphic neighboring points, and takes the average value as the membership degree of the sample. Then, the same approach is used to compute the gray relational degree between each sample and its K heterogeneous neighboring points, and the average value is taken as the non-membership degree of the sample. Finally, each sample is assigned with an appropriate fuzzy value based on intuitionistic fuzzy sets using a specific scoring function. Test results on UCI datasets show that KGRA-IFSVM has better classification performance and stronger noise resistance.