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Case-Based Reasoning for Explaining Probabilistic Machine Learning

Fulltext:


Authors:

Tomas Olsson, Daniel Gillblad, Peter Funk, Ning Xiong

Research groups:

Artificial Intelligence and Intelligent Systems
Learning and Optimisation

Publication Type:

Journal article

Venue:

International Journal of Computer Science and Information Technology

DOI:

10.5121/ijcsit

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.

Bibtex

@article{Olsson3588,
author = {Tomas Olsson and Daniel Gillblad and Peter Funk and Ning Xiong},
title = {Case-Based Reasoning for Explaining Probabilistic Machine Learning},
volume = {6},
number = {2},
pages = {87--101},
month = {April},
year = {2014},
journal = {International Journal of Computer Science and Information Technology},
url = {http://www.es.mdu.se/publications/3588-}
}