Name
Abstract | ||
|---|---|---|
Software development effort estimation is the process of predicting the effort required to develop a software system. In order to improve the estimation accuracy, many different models have been proposed in the literature. Multiple classification systems represent an important field of research for machine learning. In order to estimate software development effort, this paper proposes a heterogeneous and dynamic ensemble selection model, composed by a set of regressors dynamically selected by classifiers. Along with the proposed method it is conducted an experimental analysis involving a relevant set of software effort estimation problems, which has led to better results than those achieved by classical and state of the art models previously presented. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICTAI.2017.00042 | 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) |
Keywords | Field | DocType |
Dynamic ensemble selection,Regression,Software effort estimation | Ensemble selection,Computer science,Software system,Software,Software development effort estimation,Artificial intelligence,Machine learning,Software development,Multiple classification | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-5386-3877-4 | 0 |
PageRank | References | Authors |
0.34 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Thiago H. de A. Cabral | 1 | 0 | 0.34 |
| Ricardo De A. Araújo | 2 | 248 | 19.46 |
| Jarley Palmeira Nóbrega | 3 | 22 | 2.50 |
| Adriano L. I. Oliveira | 4 | 364 | 36.36 |