Authors
Daniel Rodriguez, Israel Herraiz, Rachel Harrison, Javier Dolado, José C Riquelme
Publication date
2014/5/13
Book
Proceedings of the 18th international conference on evaluation and assessment in software engineering
Pages
1-10
Description
Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.
Further Results and replication package …
Scholar articles
D Rodriguez, I Herraiz, R Harrison, J Dolado… - Proceedings of the 18th international conference on …, 2014