Optimizing SVR using Local Best PSO for Software Effort Estimation
| Autor: | Dinda Novitasari, Imam Cholissodin, Wayan Firdaus Mahmudy |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | JITeCS (Journal of Information Technology and Computer Science), Vol 1, Iss 1 (2016) |
| Druh dokumentu: | article |
| ISSN: | 2540-9433 2540-9824 |
| DOI: | 10.25126/jitecs.2016117 |
| Popis: | Abstract. In the software industry world, it’s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy. Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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