SODA

Case-Based Reasoning for Explaining Probabilistic Machine Learning

Olsson, Tomas and Gillblad, Daniel and Funk, Peter and Xiong, Ning (2014) Case-Based Reasoning for Explaining Probabilistic Machine Learning. International Journal of Computer Science and Information Technology, 6 (2). pp. 87-101.

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Official URL: http://airccse.org/journal/jcsit/6214ijcsit06.pdf

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.

Item Type:Article
ID Code:5701
Deposited By:Tomas Olsson
Deposited On:25 Aug 2014 13:31
Last Modified:25 Aug 2014 13:31

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