Publication:
Minimizing Classification Errors in Imbalanced Dataset Using Means of Sampling

Simple item page
dc.citedby1
dc.contributor.authorKhan I.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.contributor.authorid56727803900en_US
dc.date.accessioned2023-05-29T09:10:49Z
dc.date.available2023-05-29T09:10:49Z
dc.date.issued2021
dc.descriptionClassification (of information); Learning algorithms; Students; Class imbalance; Data level; Over sampling; Performance prediction; SMOTE; Spread subsampling; Student performance; Student performance prediction; Under-sampling; Machine learningen_US
dc.description.abstractClassification, a significant application of machine learning, labels each instance of the dataset into one of the predefined classes. Problems occur when the number of instances in the classes is not uniform. The exceptional lyuneven class distribution gives rise to class imbalancing issues which tend to demote the overall performance of the classifier. A set of data-level algorithms are available which are applied to adjust the class distribution. The class imbalancing emerges frequently in datasets from educational domains where the number of students with unsatisfactory performance general appears in low number comparing to the students with satisfactory outcomes. This paper applies a set of data-level sampling algorithms over a dataset taken from an educational domain. It underlines the consequences rising from classification with imbalanced dataset. This research confirms that a classification model achieving higher accuracy may not appear effective in correct identification of instances in minority class. Classification with an imbalance dataset may produce low recall, precision and F-Measure for classes with lower number of instances. The performance of classification model improves with application of data level algorithm. However, it highlights the supremacy of oversampling algorithm over undersampling algorithms. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_38
dc.identifier.epage446
dc.identifier.scopus2-s2.0-85120523452
dc.identifier.spage435
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120523452&doi=10.1007%2f978-3-030-90235-3_38&partnerID=40&md5=9f40e54fe9a37bbd13aa3f30e8eadb05
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26465
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleMinimizing Classification Errors in Imbalanced Dataset Using Means of Samplingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS