Optimizing SVR using Local Best PSO for Software Effort Estimation | Zendy

Dinda Novitasari | Zendy; Imam Cholissodin | Zendy; Wayan Firdaus Mahmudy | Zendy

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Optimizing SVR using Local Best PSO for Software Effort Estimation

Author(s) -

Dinda Novitasari,

Imam Cholissodin,

Wayan Firdaus Mahmudy

Publication year - 2016

Publication title -

journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2540-9824

pISSN - 2540-9433

DOI - 10.25126/jitecs.2016117

Subject(s) - computer science , particle swarm optimization , software , feature selection , machine learning , support vector machine , selection (genetic algorithm) , artificial intelligence , feature (linguistics) , mathematical optimization , estimation , data mining , mathematics , engineering , philosophy , linguistics , programming language , systems engineering

. 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

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