An empirical study to improve multiclass classification using hybrid ensemble approach for students’ performance prediction
Improving machine learning algorithms has been the interest of data scientists and researchers for the past few years. Among the performance problems raised is the classification imbalance issues listed as the top ten. The present study makes comparison and analysis of 5 state-of-art classifiers, 5...
Main Authors: | Hassan, Hasniza, Ahmad, Nor Bahiah, Sallehuddin, Roselina |
---|---|
Format: | Conference or Workshop Item |
Published: |
2021
|
Subjects: |
Similar Items
-
An empirical study to improve multiclass classification using hybrid ensemble approach for students' performance prediction
by: Hassan, H., et al.
Published: (2021) -
Ensemble classifier and resampling for imbalanced multiclass learning
by: Sainin, Mohd Shamrie, et al.
Published: (2015) -
Combining sampling and ensemble classifier for multiclass imbalance data learning
by: Sainin, Mohd Shamrie, et al.
Published: (2018) -
Hybrid particle swarm optimization feature selection for crime classification
by: Anuar, Syahid, et al.
Published: (2015) -
Ensemble Meta Classifier with Sampling and Feature Selection for Data with Multiclass Imbalance Problem
by: Sainin, Mohd Shamrie, et al.
Published: (2021)