Comparison of Bagging and Boosting in Imbalanced Multilabel of Al-Quran Dataset
Author:
Affiliation:
1. School of Computing Telkom University,Bandung,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10037243/10037256/10037462.pdf?arnumber=10037462
Reference26 articles.
1. Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
2. A Multi-label Classification on Topics of Quranic Verses (English Translation) Using Backpropagation Neural Network with Stochastic Gradient Descent and Adam Optimizer
3. Indonesian Translation of the Holy Quran (Multi-label);said al faraby;Dataverse Telkom University,2021
4. A Multi-Label Classification on Topics of Quranic Verses in English Translation Using Tree Augmented Naïve Bayes
5. An Ensemble Multi-label Themes-Based Classification for Holy Qur’an Verses Using Word2Vec Embedding
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1. Prediction of Hypertension in the Upcoming Year: Feature Correlation Analysis and Handling Imbalanced Based on Random Forest;2023 Eighth International Conference on Informatics and Computing (ICIC);2023-12-08
2. Improvement of Imbalanced Data Handling: A Hybrid Sampling Approach by using Adaptive Synthetic Sampling and Tomek links;2023 Eighth International Conference on Informatics and Computing (ICIC);2023-12-08
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