Publication:
Taguchi?s T-method with Normalization-Based Binary Bat Algorithm

Simple item page
dc.citedby0
dc.contributor.authorMarlan Z.M.en_US
dc.contributor.authorJamaludin K.R.en_US
dc.contributor.authorHarudin N.en_US
dc.contributor.authorid57223885180en_US
dc.contributor.authorid26434395500en_US
dc.contributor.authorid56319654100en_US
dc.date.accessioned2025-03-03T07:48:09Z
dc.date.available2025-03-03T07:48:09Z
dc.date.issued2024
dc.description.abstractTaguchi?s T-method (T-method) is a predictive modeling technique developed by Dr. Genichi Taguchi under the Mahalanobis-Taguchi system to predict unknown output or future state based on multivariable input variables. Conventionally, Taguchi?s orthogonal array is used as a variable selection approach in optimizing the predictive model. Due to a fixed and restricted predetermined design array, the orthogonal array is unable to give higher-order interactions between variables, resulting in inferior T-method prediction accuracy. Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. Specifically, a normalization-based Binary Bat algorithm is used, where discretization of continuous solution into binary form is performed using a normalization equation. An experimental study was conducted, and the variable selection process using the normalization-based Binary Bat algorithm found a better combination of input variables which consists of only six out of eight variables. The prediction accuracy was also enhanced by 7.15% when validated using the validation dataset. In conclusion, the proposed method successfully yields better prediction accuracy as compared to conventional approaches. After the variable selection process using the proposed method, the optimal prediction model is now formulated with a lesser variable, making it less complex and computationally fast. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-53960-2_27
dc.identifier.epage428
dc.identifier.scopus2-s2.0-85189550364
dc.identifier.spage414
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85189550364&doi=10.1007%2f978-3-031-53960-2_27&partnerID=40&md5=fd4f0f55dd0e9aa4df19952a5d35b860
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37163
dc.identifier.volume919 LNNS
dc.pagecount14
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectMultivariable systems
dc.subjectBat algorithms
dc.subjectBinary bat algorithm
dc.subjectDiscretizations
dc.subjectInput variables
dc.subjectNormalisation
dc.subjectOrthogonal array
dc.subjectPrediction accuracy
dc.subjectPredictive models
dc.subjectTaguchi?s T-method
dc.subjectVariables selections
dc.subjectForecasting
dc.titleTaguchi?s T-method with Normalization-Based Binary Bat Algorithmen_US
dc.typeConference paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS