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A Comparison of Machine Learning Methods for Software Effort Estimation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Polat, Aydın Göze Yigit, Sevgi |
| Copyright Year | 2013 |
| Abstract | In this study we aimed to draw a big comparative picture of the state of the art machine learning approaches for the software effort estimation problem. For this purpose, several datasets which were obtained from Promise data repository were used for testing various machine learning techniques. The results showed that, decision trees or rule induction based classifiers (i.e. M5P trees) gave particularly good results for more than one dataset. Moreover for certain datasets the best results were achieved by other type of classifiers such as K*. Meta-classifiers such as Additive Regression, when combined with M5P trees, gave the best results in our tests. Keywords—effort estimation, machine learning methods, NASA projects, CHINA projects, PROMISE projects, WEKA. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://users.metu.edu.tr/e163109/MachineLearningTechniquesForEffortEstimation.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
