Letter Recognition Using Holland-Style Adaptive Classifiers
Machine Learning
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In the study of letter recognition, the recognition accuracy is impacted by fonts and styles, which is the main bottleneck that the technology is applied. In order to enhance the accuracy, a letter recognition algorithm based artificial immune, referred to as LEBAI, is presented. Inspired by nature immune system, antibody cell (B-cell) population is evolved until the B-cell population is convergent through the learning of each training antigen and the memory cells pool is updated by the optimal B-cell. Finally, recognition is accomplished by memory cells. It is tested by the well-known letter recognition data set of UCI (University of California at Irvine). Compared with HSAC (Letter Recognition Using Holland-Style Adaptive Classifiers), LEBAI showed that recognition accuracy is increased from 82.7% to 95.58%. LEBAI achieves the same recognition accuracy for the letters of different fonts and styles, or stretched and distorted randomly.