Evaluation of machine learning algorithms on academic big dataset by using feature selection techniques

Basit Öğe Kaydı
dc.authorscopusid57216577687
dc.authorscopusid57212846264
dc.authorscopusid56510992300
dc.authorscopusid56522820200
dc.contributor.authorKumar M.
dc.contributor.authorSingh A.J.
dc.contributor.authorSharma B.
dc.contributor.authorCengiz K.
dc.date.accessioned2024-06-12T10:29:41Z
dc.date.available2024-06-12T10:29:41Z
dc.date.issued2022
dc.description.abstractIdentifying the most accurate methods for forecasting students’ academic achievement is the focus of this research. Globally, all educational institutions are concerned about student attrition. The goal of all educational institutions is to increase the student’s retention and graduation rates and this is only possible if at-risk students are identified early. Due to inherent classifier constraints and the incorporation of fewer student features, most commonly used prediction models are inefficient and incur. Different data mining algorithms like classification, clustering, regression, and association rule mining are used to uncover hidden patterns and relevant information in student performance big datasets in academics. Naïve Bayes, random forest, decision tree, multilayer perceptron (MLP), decision table (DT), JRip, and logistic regression (LR) are some of the data mining techniques that can be applied. A student’s academic performance big dataset comprises many features, none of which are relevant or play a significant role in the mining process. So, features with a variance close to 0 are removed from the student’s academic performance big dataset because they have no impact on the mining process. To determine the influence of various attributes on the class level, various feature selection (FS) techniques such as the correlation attribute evaluator (CAE), information gain attribute evaluator (IGAE), and gain ratio attribute evaluator (GRAE) are utilized. In this study, authors have investigated the performance of various data mining algorithms on the big dataset, as well as the effectiveness of various FS techniques. In conclusion, each classification algorithm that is built with some FS methods improves the performance of the classification algorithms in their overall predictive performance. © The Institution of Engineering and Technology 2022.en_US
dc.identifier.endpage92en_US
dc.identifier.isbn9781839535338
dc.identifier.isbn9781839535345
dc.identifier.scopus2-s2.0-85158977296en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage61en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/17883
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitution of Engineering and Technologyen_US
dc.relation.ispartofIntelligent Network Design Driven by Big Data Analytics, IoT, AI and Cloud Computingen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBig Data; Feature Selection; Classification; Correlation Attribute Evaluator; Data Mining; Gain Ratio Attribute Evaluator; Information Gain Attribute Evaluatoren_US
dc.titleEvaluation of machine learning algorithms on academic big dataset by using feature selection techniquesen_US
dc.typeBook Chapteren_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu