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Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering by Rajbahadur, Gopi Krishnan; Wang, Shaowei; Kamei, Yasutaka; Hassan, Ahmed E. is a scholarly article available to read on EtoBox.

What is Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering about?

Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noi

Author

Rajbahadur, Gopi Krishnan; Wang, Shaowei; Kamei, Yasutaka; Hassan, Ahmed E.

Published

2022

Language

EN