This paper changes the assumption that samples are independent and identically distributed to that samples are uniformly ergodic Markov chains

which make it convenient for us to study the generalization performance of the imbalanced data classification algorithm

and SVM imbalanced data classification algorithm based on Markov sampling

EDSVM imbalanced data classification algorithm based on Markov sampling and SVM-WKNN imbalanced data classification algorithm based on Markov sampling are proposed. The numerical experiments of ten actual imbalanced datasets in the UCI database show that the misclassification rate of the above algorithm based on Markov sampling is lower than that of the corresponding algorithm based on random sampling

and the above three algorithms

SVM-WKNN imbalanced data classification algorithm based on Markov sampling has the best generalization performance.