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Published in: Empirical Software Engineering 2/2019

25-07-2018

Balancing the trade-off between accuracy and interpretability in software defect prediction

Authors: Toshiki Mori, Naoshi Uchihira

Published in: Empirical Software Engineering | Issue 2/2019

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Abstract

Context

Classification techniques of supervised machine learning have been successfully applied to various domains of practice. When building a predictive model, there are two important criteria: predictive accuracy and interpretability, which generally have a trade-off relationship. In particular, interpretability should be accorded greater emphasis in the domains where the incorporation of expert knowledge into a predictive model is required.

Objective

The aim of this research is to propose a new classification model, called superposed naive Bayes (SNB), which transforms a naive Bayes ensemble into a simple naive Bayes model by linear approximation.

Method

In order to evaluate the predictive accuracy and interpretability of the proposed method, we conducted a comparative study using well-known classification techniques such as rule-based learners, decision trees, regression models, support vector machines, neural networks, Bayesian learners, and ensemble learners, over 13 real-world public datasets.

Results

A trade-off analysis between the accuracy and interpretability of different classification techniques was performed with a scatter plot comparing relative ranks of accuracy with those of interpretability. The experiment results show that the proposed method (SNB) can produce a balanced output that satisfies both accuracy and interpretability criteria.

Conclusions

SNB offers a comprehensible predictive model based on a simple and transparent model structure, which can provide an effective way for balancing the trade-off between accuracy and interpretability.

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Appendix
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Metadata
Title
Balancing the trade-off between accuracy and interpretability in software defect prediction
Authors
Toshiki Mori
Naoshi Uchihira
Publication date
25-07-2018
Publisher
Springer US
Published in
Empirical Software Engineering / Issue 2/2019
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-018-9638-1

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