Review to discuss. Received from: AI Communications, IOS Press. Paper title: Mapping Learning Algorithms on Data, a useful step for optimizing performances and their comparison. Author: F. Neri

Abstract In the paper, we propose a novel methodology to map learning algorithms on data (performance map) in order to gain more insights in the distribution of their performances across their parameter space. This methodology provides useful information when selecting a learner’s best configuration for the data at hand, and it also enhances the comparison of learners across learning contexts. In order to explain the proposed methodology, the study introduces the notions of learning context, performance map, and high performance function. It then applies these concepts to a variety of learning contexts to show how their use can provide more insights in a learner’s behavior, and can enhance the comparison of learners across learning contexts. The study is completed by an extensive experimental study describing how the proposed methodology can be applied.

Reply from the author on 15/07/2021 Dear Prof. Schockaert (Editor in chief of AI Communications), thank you for letting me know about the decision of my paper. However, I believe that the received review is not fair for a number of reasons. Could you please read personally my paper, the performend review and my replies below and make an informed decision about? I would like to please ask that you send the paper for review again. But I will accept your next decision. Please let me know asap. Best regards, Filippo Neri

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