EVISClass: a new evaluation method for image data stream classifiers
Mateus C. de Lima, Maria Camila N. Barioni, Elaine R. Faria, Humberto Razente · 2020
Methods for image data stream classification need to update their model constantly and many of these perform this in a supervised way. However, these studies evaluate the performance of their methods assuming that all labels will be available immediately after classification, which is not consistent with various real-world application scenarios. This article proposes a new evaluation method for image data stream classifiers that allows for the exploration of different issues present in real-world applications, such as the emergence of new classes, the evolution of existing classes, and delayed image labels after classification. Through an analysis of the experimental results, we verified that the proposed evaluation method allowed the identification of the issues that most impact the accuracy of the image classifier, indicating a need to direct efforts in carrying out future works to develop strategies to mitigate these issues.