Journal
KNOWLEDGE-BASED SYSTEMS
Volume 220, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2021.106916
Keywords
Explainable AI; Interpretable Machine Learning (ML); Interactive ML; Aggregation functions; Ordinal sums; Glass-box; Transparency
Categories
Funding
- Austrian Science Fund (FWF) [P-32554]
- Ministry of Education, Science, Research and Sport of the Slovak Republic [VEGA 1/0466/19, VEGA 1/0006/19]
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The study introduced a novel classification method that empowers domain experts to choose important observations for attributes and utilizes function variability for machine learning opportunities. Demonstrated the research steps of human-in-the-loop interactive machine learning with aggregation functions.
We propose a novel classification according to aggregation functions of mixed behaviour by variability in ordinal sums of conjunctive and disjunctive functions. Consequently, domain experts are empowered to assign only the most important observations regarding the considered attributes. This has the advantage that the variability of the functions provides opportunities for machine learning to learn the best possible option from the data. Moreover, such a solution is comprehensible, reproducible and explainable-per-design to domain experts. In this paper, we discuss the proposed approach with examples and outline the research steps in interactive machine learning with a human-in-the-loop over aggregation functions. Although human experts are not always able to explain anything either, they are sometimes able to bring in experience, contextual understanding and implicit knowledge, which is desirable in certain machine learning tasks and can contribute to the robustness of algorithms. The obtained theoretical results in ordinal sums are discussed and illustrated on examples. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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