Publication:
Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method

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.accessioned2024-10-14T03:19:34Z
dc.date.available2024-10-14T03:19:34Z
dc.date.issued2023
dc.description.abstractPrediction modeling has emerged as a powerful tool in various fields, from healthcare to finance, climate science to marketing. One of the prediction modelling techniques available is known as Taguchi's T-method introduced by Dr. Genichi Taguchi. In the T-method prediction model, optimization of the model's accuracy is performed through feature selection process by utilizing an orthogonal array. However, the outcome yielded a sub-optimal result as the orthogonal array has limitation involving a fixed and limited combination used and lack of higher order feature combination in the analysis. Thus, this study proposed an Opposition-based Learning Binary Bat Algorithm as the feature selection technique in the T-method. Based on the experimental results, the proposed feature selection method successfully found a superior combination that yields a better result in terms of the objective function. The proposed method recorded a 77.8% reduction rate of the number of features from 18 to 4. In terms of prediction accuracy, the new T-method prediction model successfully improved 15.9% as compared to the model without feature selection and the T-method with conventional orthogonal array approach. These results suggest that the new T-method prediction model is better in predicting the output even when only 4 features incorporated in the model. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSPC59664.2023.10420191
dc.identifier.epage112
dc.identifier.scopus2-s2.0-85186661092
dc.identifier.spage107
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85186661092&doi=10.1109%2fICSPC59664.2023.10420191&partnerID=40&md5=61194d9d651a445d51d63690dcf1d2b3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34407
dc.pagecount5
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
dc.subjectBinary Bat Algorithm
dc.subjectFeature Selection
dc.subjectOpposition-Based Learning
dc.subjectPrediction Model
dc.subjectTaguchi's T-method
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectLearning systems
dc.subjectBat algorithms
dc.subjectBinary bat algorithm
dc.subjectClimate science
dc.subjectFeatures selection
dc.subjectModel optimization
dc.subjectModelling techniques
dc.subjectOpposition-based learning
dc.subjectOrthogonal array
dc.subjectPrediction modelling
dc.subjectTaguchi T-method
dc.subjectFeature Selection
dc.titleOpposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Methoden_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS