Optimizing SVR using Local Best PSO for Software Effort Estimation

Abstract

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

Classification Tuberculosis DNA using LDA-SVM

“…So, the output of the research is to prove and get the right k value from k-Mer to extract the feature. After that, the machine learning algorithm will be used in this paper which is Support Vector Machine (SVM) to classify TB DNA, because its good performance in classification and has successfully applied to many classification problems, and its advantage that SVM could avoid overfitting and being able to generalize data properly [11] [12].…”