Fuzzy support vector machine using local outlier factor and intuitionistic fuzzy sets for imbalanced datasets
Mengya Hu, Shaowu Lu · Journal of Control and Decision · 2024
Traditional classifiers are commonly used for solving class balance problems. However, many datasets exhibit class imbalance along with outliers and noise, which affect the classification accuracy, this paper proposes a fuzzy support vector machine algorithm based on local outlier factor and intuitionistic fuzzy sets. First, to highlight the importance of the minority class, the membership degree is set to the maximum value. Meanwhile, local outlier factor is calculated to measure the abnormality and further obtain the membership degree in the majority class. Then, a kernel density estimation method is designed to estimate the sample density accurately. Furthermore, based on the density distribution, intuitionistic fuzzy sets are used to assign membership and non-membership degrees to samples, effectively distinguishing between noise and outliers. Finally, different penalty coefficients for two classes are built to offset the impact of class imbalance on classification accuracy. Experimental results show the advantages of the proposed algorithm.