The design of error-correcting output codes algorithm for the open-set recognition.

The Open-Set recognition is an important topic in the pattern recognition research field. Different from the close-set recognition task, in the open-set recognition problem, the test data contains unknown classes that do not appear in the training phase. Consequently, the recognition of the open-set data is much more difficult than that of the close-set problem. This study applies the Error-Correcting Output Codes (ECOC) framework to handle the open-set problem by dynamically adding new functions to deal with the unknown classes, named ECOC-OS. Our algorithm includes two steps: (1) the unknown data discovery step based on a rejection strategy; (2) the code matrix expanding step for the separation of the unknown classes from the known classes. Due to the wide and chaotic distribution of the unknown class samples, this paper refines the unknown class into multiple sub-classes, and each sub-class has its own feature distribution. After preliminary row and column expansion and class splitting for the unknown class, the clustering algorithm is used to continuously refine the characteristics of the unknown class, dividing it into several sub-classes. Then the algorithm adds multiple coding rows and multiple "one-to-all" basic classifiers, so as to distinguish each unknown sub-class from multiple known classes. Finally, without re-training the existing learners, the zero symbols in the code matrix are selectively re-encoded according to the basic learners' preference. The experiments deploy 16 data sets for the test, and the results confirm that ECOC-OS algorithm effectively improves the performance compared with other open-set recognition methods. [ABSTRACT FROM AUTHOR]