In this paper, an efficient classification methodology is developed for reliability analysis while maintaining an accuracy level similar to or better than existing response surface methods. The sampling-based reliability analysis requires only the classification information-a success or a failure-but the response surface methods provide function values on the domain as their output, which requires more computational effort. The problem is even more challenging when dealing with high-dimensional problems due to the curse of dimensionality. In the newly proposed virtual support vector machine (VSVM), virtual samples are generated near the limit state function by using an approximation method. The function values are used for approximations of virtual samples to improve accuracy of the resulting VSVM decision function. By introducing the virtual samples, VSVM can overcome the deficiency in existing classification methods where only classification values are used as their input. The universal Kriging method is used to obtain virtual samples to improve the accuracy of the decision function for highly nonlinear problems. A sequential sampling strategy that chooses new samples near the limit state function is integrated with VSVM to improve the accuracy. Examples show the proposed adaptive VSVM yields better efficiency in terms of modeling and response evaluation time and the number of required samples while maintaining similar level or better accuracy, especially for high-dimensional problems.