Name
Abstract | ||
|---|---|---|
This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to a specified confidence level. Our approach is based on the well-known Ridge Regression technique, but instead of the simple effort estimates produced by the original method, it produces predictive intervals that satisfy a given confidence level. The results obtained using the proposed algorithm on the COCOMO, Desharnais and ISBSG datasets suggest a quite successful performance obtaining reliable predictive intervals which are narrow enough to be useful in practice. |
Year | Venue | Keywords |
|---|---|---|
2009 | AIAI Workshops | confidence interval |
Field | DocType | Citations |
Confidence measures,Regression,Computer science,Software,Prediction algorithms,Artificial intelligence,COCOMO,Confidence interval,Machine learning | Conference | 15 |
PageRank | References | Authors |
0.72 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harris Papadopoulos | 1 | 219 | 26.33 |
| Efi Papatheocharous | 2 | 133 | 21.97 |
| Andreas S. Andreou | 3 | 216 | 36.65 |