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

Abstract

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.