A Man-Machine Cooperating System Based on the Generalized Reject Model

In recognition systems, reject options are usually introduced to reduce error rates for general classifiers. For taking this option, there is a trade-off relationship between error rates and reject rates and it is required to optimize the trade-off. Conventional methods have implicit assumptions that the error rates are zero after the rejection; however, real systems have their own error rates even after the rejection. In this paper, we propose a generalized reject model that can introduce error rates after rejection. This model can handle variety of systems with plural classifiers and thresholds. Also, we can optimize the error-reject trade-off by defining and minimizing a cost function of the model. Finally, experimental results show effectiveness of the proposed model by applying it to data entry systems.