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Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

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dc.contributor.authorZakaria A.Z.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57210731675en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid57188850203en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T09:38:45Z
dc.date.available2023-05-29T09:38:45Z
dc.date.issued2022
dc.descriptionBalancing; Classification (of information); Digital storage; Machine learning; Random forests; And machine learning; Class imbalance; Classification performance; Classification technique; Detection performance; Machine-learning; Misclassifications; Random forests; Resamples; Research focus; Data miningen_US
dc.description.abstractData mining is a knowledge discovery of the data that extracts and discovers patterns and relationships to predict outcomes. Class imbalance is one of the obstacles that can drive misclassification. The class imbalance affected the result of classification machine learning. The classification technique can divide the data into the given class target. This research focuses on four pre-processing methods: SMOTE, Spread Subsample, Class Balancer, and Resample. These methods can help to clean the data before undergoing the classification techniques. Resample shows the best result for solving the imbalance problem with 41.321 for Mean and Standard Deviation, 64.101. Besides, this research involves six classification techniques: Na�ve Bayes, BayesNet, Random Forest, Random Tree, Logistics, and Multilayer Perceptron. Indeed, the combination of Resample and Random Forest has the best result of Precision, 0.941, and ROC Area is 0.983. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO56118.2022.10031922
dc.identifier.epage323
dc.identifier.scopus2-s2.0-85148421899
dc.identifier.spage316
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85148421899&doi=10.1109%2fICOCO56118.2022.10031922&partnerID=40&md5=60d1e2d75b64921abbe14c1da66dae8f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27021
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE International Conference on Computing, ICOCO 2022
dc.titleImproving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Foresten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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