Classifier combination for hand-printed digit recognition
Michael Sabourin, Amar Mitiche, D. Thomas, George Nagy · 2002
Independent decisions by two high performance nearest-neighbor hand-printed digit classifiers are combined in a principled manner. Three combination methods are investigated: Bayesian combination, Dempster-Shafer evidential reasoning, and dynamic classifier selection. On a test set of 60,000 hand-printed digits, dynamic classifier selection performs slightly better than Bayesian or Dempster-Shafer evidential reasoning, but the lowest error rate is obtained by K-nearest-neighbor combination. Single-parameter classifier combination is used to generate error-reject curves. Essential error-free classification is obtained at the cost of 4% rejects. The zero-reject error rate decreases from 1.18% for the best single classifier system to 0.67% for the combined classifier.>