On Interpretability and Similarity in Concept-Based Machine Learning

Kwuida, Léonard; Ignatov, Dmitry (2021). On Interpretability and Similarity in Concept-Based Machine Learning In: International Conference on Analysis of Images, Social Networks and Texts AIST 2020: Analysis of Images, Social Networks and Texts 12602 (pp. 28-54). Cham: Springer Nature Switzerland 10.1007/978-3-030-72610-2_3

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Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed: - How does an ML procedure derive the class for a particular entity? - Why does a particular clustering emerge from a particular unsupervised ML procedure? - What can we do if the number of attributes is very large? - What are the possible reasons for the mistakes for concrete cases and models? For binary attributes, Formal Concept Analysis (FCA) offers techniques in terms of intents of formal concepts, and thus provides plausible reasons for model prediction. However, from the interpretable machine learning viewpoint, we still need to provide decision-makers with the importance of individual attributes to the classification of a particular object, which may facilitate explanations by experts in various domains with high-cost errors like medicine or finance. We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.

Item Type:	Book Section (Book Chapter)
Division/Institute:	Business School > Business Foundations and Methods
Name:	Kwuida, Léonard0000-0002-9811-0747 and Ignatov, Dmitry
ISBN:	978-3-030-72609-6
Publisher:	Springer Nature Switzerland
Language:	English
Submitter:	Léonard Kwuida
Date Deposited:	17 Aug 2021 15:32
Last Modified:	17 Aug 2021 15:32
Publisher DOI:	10.1007/978-3-030-72610-2_3
URI:	https://arbor.bfh.ch/id/eprint/15212