{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:58:44Z","timestamp":1774965524666,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643681146","type":"print"},{"value":"9781643681153","type":"electronic"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:p>Student performance is the most factor that can be beneficial for many parties, including students, parents, instructors, and administrators. Early prediction is needed to give the early monitor by the responsible person in charge of developing a better person for the nation. In this paper, the improvement of Bagged Tree to predict student performance based on four main classes, which are distinction, pass, fail, and withdrawn. The accuracy is used as an evaluation parameter for this prediction technique. The Bagged Tree with the addition of Bag, AdaBoost, RUSBoost learners helps to predict the student performance with the massive datasets. The use of the RUSBoost algorithm proved that it is very suitable for the imbalance datasets as the accuracy is 98.6% after implementing the feature selection and 99.1% without feature selection compared to other learner types even though the data is more than 30,000 datasets.<\/jats:p>","DOI":"10.3233\/faia200552","type":"book-chapter","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T22:59:58Z","timestamp":1600297198000},"source":"Crossref","is-referenced-by-count":5,"title":["The Best Ensemble Learner of Bagged Tree Algorithm for Student Performance Prediction"],"prefix":"10.3233","author":[{"given":"Afiqah Zahirah","family":"Zakaria","sequence":"first","affiliation":[{"name":"Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia"}]},{"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, Malaysia"},{"name":"School of Computing, Faculty of Engineering, UTM & Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, Johor Bahru, Malaysia"},{"name":"Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003 Hradec Kralove, Czech Republic"}]},{"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[{"name":"Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate 020-0693, Japan"}]},{"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, 50003 Hradec Kralove, Czech Republic"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,17]],"date-time":"2020-09-17T13:26:27Z","timestamp":1600349187000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,15]]},"ISBN":["9781643681146","9781643681153"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200552","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,15]]}}}