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Performance of machine learning models using original featu..:
Nihal Abuzinadah
;
Muhammad Umer
;
Abid Ishaq
...
doi:10.1371/journal.pone.0293061.t003. , 2023
Link:
https://doi.org/10.1371/journal.pone.0293061.t003
RT Journal T1
Performance of machine learning models using original features
UL https://suche.suub.uni-bremen.de/peid=base-ftdeakinunifig:oai:figshare.com:article_24527772&Exemplar=1&LAN=DE A1 Nihal Abuzinadah A1 Muhammad Umer A1 Abid Ishaq A1 Abdullah Al Hejaili A1 Shtwai Alsubai A1 Ala' Abdulmajid Eshmawi A1 Abdullah Mohamed A1 Imran Ashraf YR 2023 K1 Science Policy K1 Space Science K1 Biological Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 students &# 8217 K1 results obtained using K1 extra tree classifier K1 experimental results indicate K1 also evaluated using K1 offering substantial advancements K1 imbalanced datasets using K1 deep convoluted features K1 convoluted features show K1 highest classification accuracy K1 mine educational data K1 educational data mining K1 predict student performance K1 analyzing student performance K1 student performance prediction K1 imbalanced data K1 student failure K1 educational settings K1 substantial volume K1 data originating K1 convolutional features K1 academic performance K1 limited accuracy K1 accuracy compared K1 utmost importance K1 uncover insights K1 study proposes K1 smote ) K1 research introduces K1 provide measures K1 proposed framework K1 original features K1 machine learning K1 learn better K1 help reduce K1 feature engineering K1 existing methods K1 driven system K1 devises techniques K1 based system K1 artificial intelligence K1 art approaches K1 9 % JF doi:10.1371/journal.pone.0293061.t003 LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0293061.t003 DO https://doi.org/10.1371/journal.pone.0293061.t003 SF ELIB - SuUB Bremen
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