EASE 2011 : 15th Annual Conference on Evaluation & Assessment in Software Engineering : 11-12 April 2011.
Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class dist...
Gespeichert in:
- Körperschaft:
- Format:
- Elektronisch E-Book
- Sprache:
- Englisch
- Veröffentlicht:
-
Stevenage, England :
IET,
2011.
- Zusammenfassung:
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Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why failing to report precision when using data with highly imbalanced class distributions may provide an overly optimistic view of classifier performance. Method: Well documented examples of how dependent class distribution affects the suitability of performance measures. Conclusions: When using data where the minority class represents less than around 5 to 10 percent of data points in total, failing to report precision may be a critical mistake. Furthermore, deriving the precision values omitted from studies can reveal valuable insight into true classifier performance.
- Umfang:
- 1 online resource (168 pages)
- ISBN:
- 1-84919-509-9
- Schlagworte:
- Links: