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Haas, Stefan ORCID logoORCID: https://orcid.org/0000-0001-9916-0060 and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (18. September 2023): Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Torino, Italy, 18-22 September, 2023. In: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, Vol. 14174 pp. 3-18

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This paper proposes a novel approach for modeling observational data in the form of expert ratings, which are commonly given on an ordered (numerical or ordinal) scale. In practice, such ratings are often biased, due to the expert’s preferences, psychological effects, etc. Our approach aims to rectify these biases, thereby preventing machine learning methods from transferring them to models trained on the data. To this end, we make use of so-called label smoothing, which allows for redistributing probability mass from the originally observed rating to other ratings, which are considered as possible corrections. This enables the incorporation of domain knowledge into the standard cross-entropy loss and leads to flexibly configurable models. Concretely, our method is realized for ordinal ratings and allows for arbitrary unimodal smoothings using a binary smoothing relation. Additionally, the paper suggests two practically motivated smoothing heuristics to address common biases in observational data, a time-based smoothing to handle concept drift and a class-wise smoothing based on class priors to mitigate data imbalance. The effectiveness of the proposed methods is demonstrated on four real-world goodwill assessment data sets of a car manufacturer with the aim of automating goodwill decisions. Overall, this paper presents a promising approach for modeling ordinal observational data that can improve decision-making processes and reduce reliance on human expertise.

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