Bursting interference that causes a sudden and significant change in surface electromyography (sEMG) characteristics, can reduce the stability and security of myoelectric assistive robots. Current adaptation strategies for progressive-generated interference are incapable of dealing with bursting interference. To address this problem, an incremental learning and fault-tolerant classifier (ILFTC) was proposed by combining a Gaussian mixture model (GMM) ensemble and linear discriminant analysis (LDA), in conjunction with online update and marginalization schemes. Subsequently, an ILFTC-based myoelectric pattern recognition (MPR) strategy was developed to improve the robustness of MPR against multiple interferences, including outlier motion and missing/fault data owing to electrode loosening. Experiments on hand/wrist motions were conducted to validate the anti-interference performance of the ILFTC. Experimental results showed that the ILFTC could effectively resist the two types of bursting interference and produce a significant improvement in the recognition performance over traditional classifiers, as well as the methods presented in previous studies. The results show that the proposed method has the potential to enhance the robustness of myoelectric assistive robots.