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Case-Based Reasoning for Explaining Probabilistic Machine Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS (Embedded Systems))ORCID iD: 0000-0002-9890-4918
SICS Swedish ICT, Kista, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
2014 (English)In: International Journal of Computer Science & Information Technology (IJCSIT), ISSN 0975-4660, E-ISSN 0975-3826, Vol. 6, no 2, 87-101 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 6, no 2, 87-101 p.
Keyword [en]
Case-based Reasoning, Case-based Explanation, Artificial Intelligence, Decision Support, Machine Learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-26442DOI: 10.5121/ijcsitOAI: oai:DiVA.org:mdh-26442DiVA: diva2:759872
Projects
ITS-EASY Post Graduate School for Embedded Software and SystemsCREATE ITEA2
Available from: 2014-10-31 Created: 2014-10-31 Last updated: 2015-09-22Bibliographically approved
In thesis
1. A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
Open this publication in new window or tab >>A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Heavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.

In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2015
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 189
Series
SICS Dissertation Series, ISSN 1101-1335 ; 73
National Category
Computer Science
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-29097 (URN)978-91-7485-234-9 (ISBN)
Public defence
2015-11-10, Omega, Mälardalens högskola, Västerås, 14:00 (English)
Opponent
Supervisors
Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2015-10-15Bibliographically approved

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