Kernel local outlier factor‐based fuzzy support vector machine for imbalanced classification

Abstract

AbstractThe problem of imbalanced data classification has become a research hotspot in the field of machine learning. Fuzzy support vector machine (FSVM) is an imbalanced classification processing method based on cost‐sensitive theory. The existing methods have cost‐sensitive, causing the prior distribution estimation of data inaccurate. This article proposes a novel FSVM algorithm based on the kernel local outlier factor (KLOF‐FSVM) for this problem. KLOF calculates the local outlier factor of the sample in the kernel space and assigns an appropriate membership value to the sample. This process enables the algorithm to obtain the distribution information of the data better. Compared with the algorithm based on distance only, KLOF has better robustness. It can expand the value range of majority class samples' membership degree to better balance the important relation between the minority class and the majority class. We selected some datasets in the Keel data repository and used cross‐validation to obtain the algorithm's effect under different evaluation indexes such as G‐Mean, F1 measure, and area under curve. By comparing with other algorithms, preliminary results show that this method has better classification quality.