A data filling method for bank direct selling missing data based on an improved EM algorithm

Aiming at the problem of missing data in bank marketing, this paper proposes an alternative approach that involves combining the EM algorithm with Gibbs sampling. Given a set of incomplete data, the proposed approach uses samples obtained by Gibbs sampling to replace missing values in the expectation step of the EM algorithm. Afterwards, the likelihood function is used to complete the expectation maximization iterative update. The missing data is filled in by repeated iterations to obtain the maximized estimate as the reconstructed value. Simulation experiments using a bank marketing campaign dataset shows that the proposed algorithm perform better than the EM algorithm in data filling while effectively addressing the susceptibility of EM to initial value settings.