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1.	Olsson, Tomas, et al. (author) Case-Based Reasoning for Explaining Probabilistic Machine Learning 2014 In: International Journal of Computer Science & Information Technology (IJCSIT). - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6:2, s. 87-101 Journal article (peer-reviewed)abstract This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

Olsson, Tomas, et al. (author)
Case-Based Reasoning for Explaining Probabilistic Machine Learning
2014
In: International Journal of Computer Science & Information Technology (IJCSIT). - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6:2, s. 87-101
Journal article (peer-reviewed)abstract
- This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

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