Cost-sensitive MQDF classifier for handwritten Chinese address recognition
To overcome the class imbalance problem in Chinese address recognition, we propose a cost-sensitive learning method for MQDF classifier. In the learning process, a cost vector is introduced to the discriminative learning process of MQDF, and minimization of misclassification cost is used as the convergence criteria. A cost-sensitive MQDF classifier (CMQDF) is then obtained, and it is integrated into a handwritten Chinese address recognition (HCAR) system to validate its effectiveness. The experimental results show that CMQDF is an effective cost-sensitive classifier for the class imbalance problem in HCAR system. Moreover, it enhances the reliability of the HCAR system.