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Basic Evaluation Scenarios for Incrementally Trained Classifiers

机译：渐进培训的分类器的基本评估方案

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摘要

Evaluation of incremental classification algorithms is a complex task because there are many aspects to evaluate. Besides the aspects such as accuracy and generalization that are usually evaluated in the context of classification, we also need to assess how the algorithm handles two main challenges of the incremental learning: the concept drift and the catastrophic forgetting. However, only catastrophic forgetting is evaluated by the current methodology, where the classifier is evaluated in two scenarios for class addition and expansion. We generalize the methodology by proposing two new scenarios of incremental learning for class inclusion and separation that evaluate the handling of the concept drift. We demonstrate the proposed methodology on the evaluation of three different incremental classifiers, where we show that the proposed methodology provides a more complete and finer evaluation.
机译：增量分类算法的评估是一个复杂的任务，因为有很多方面可以评估。除了在分类上下文中通常评估的准确性和泛化等方面，还需要评估算法如何处理增量学习的两个主要挑战：概念漂移和灾难性的遗忘。然而，只有当前方法评估灾难性的遗忘，其中分类器在两个方案中评估了课堂添加和扩展的情况。通过提出评估概念漂移的处理和分离的两个增量学习的新情景，我们概括了方法。评估概念漂移的处理。我们展示了对三种不同增量分类器的评估的提出的方法，我们表明所提出的方法提供更完整和更精细的评估。

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