Effect of Hamming Distance on Performance of ECOC with Estimated Binary Classifiers
Gendo Kumoi, Hideki Yagi, Manabu Kobayashi, Shigeichi Hirasawa · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
Error-Correcting Output Coding (ECOC) is a method for constructing a multi-valued classifier using a combination of binary classifiers. The effectiveness of ECOC for multivalued classification problems has been demonstrated by many experimental evaluations. Therefore, classification performance have strongly depended on the data under consideration, and it is not clear what kind of combinations of binary classifiers have good performance. Motivated by this fact, the authors have clarified the best combination of binary classifiers that makes ECOC, assuming a situation in which each binary classifier can estimate the true posterior probability. They also have proposed a total framework for analytical evaluation when a binary classifier outputs an estimated posterior probability that approximates the true posterior probability. These studies established a framework for evaluating the theoretical performance of ECOC.Based on these findings, this study discusses the theoretical performance of ECOC from the upper bound perspective. The results showed that increasing the Hamming distance between code words can blackuce the error rate. We then evaluate various combinations of binary classifiers based on analytical evaluation.