High-dimensional Bayesian variable selection problems are often solved using computationally expensive Markov Chain Montle Carlo (MCMC) techniques. Recently, a Bayesian variable selection technique was developed for continuous data using the EM algorithm called EMVS. We extend the EMVS method to binary data by proposing both a logistic and probit extension. To preserve the computational speed of EMVS we also implemented the Stochastic Dual Coordinate Descent (SDCA) algorithm. Further, we conduct two extensive simulation studies to show the computational speed of both methods. These simulation studies reveal the power of both methods to quickly identify the correct sparse model. When these EMVS methods are compared to Stochastic Search Variable Selection (SSVS), the EMVS methods surpass SSVS both in terms of computational speed and correctly identifying significant variables. Finally, we illustrate the effectiveness of both methods on two well-known gene expression datasets. Our results mirror the results of previous examinations of these datasets with far less computational cost.