Comparing the performance of fault prediction models which report multiple performance measures:Recomputing the confusion matrix

Bowes, D. and Hall, T. and Gray, D. (2012) Comparing the performance of fault prediction models which report multiple performance measures:Recomputing the confusion matrix. In: PROMISE '12 Proceedings of the 8th International Conference on Predictive Models in Software Engineering. ACM, pp. 109-118. ISBN 9781450312417

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Abstract

There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly comparable. This lack of comparability means that it is often difficult to evaluate the performance of one model against another. Our aim is to present an approach that allows other researchers and practitioners to transform many performance measures of categorical studies back into a confusion matrix. Once performance is expressed in a confusion matrix alternative preferred performance measures can then be derived. Our approach has enabled us to compare the performance of 600 models published in 42 studies. We demonstrate the application of our approach on several case studies, and discuss the advantages and implications of doing this.

Item Type: Contribution in Book/Report/Proceedings
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 132043
Deposited By: ep_importer_pure
Deposited On: 18 Mar 2019 11:35
Refereed?: Yes
Published?: Published
Last Modified: 11 Feb 2020 06:37
URI: https://eprints.lancs.ac.uk/id/eprint/132043

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