Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Balancing; Classification (of information); Digital storage; Machine learning; Random forests; And machine learning; Class imbalance; Classification performance; Classification technique; Detection performance; Machine-learning; Misclassifications; Random forests; Resamples; Research focus; Data min...

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Main Authors: Zakaria A.Z., Selamat A., Cheng L.K., Krejcar O.
Other Authors: 57210731675
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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Institution: Universiti Tenaga Nasional
id my.uniten.dspace-27021
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spelling my.uniten.dspace-270212023-05-29T17:38:46Z Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest Zakaria A.Z. Selamat A. Cheng L.K. Krejcar O. 57210731675 24468984100 57188850203 14719632500 Balancing; Classification (of information); Digital storage; Machine learning; Random forests; And machine learning; Class imbalance; Classification performance; Classification technique; Detection performance; Machine-learning; Misclassifications; Random forests; Resamples; Research focus; Data mining Data mining is a knowledge discovery of the data that extracts and discovers patterns and relationships to predict outcomes. Class imbalance is one of the obstacles that can drive misclassification. The class imbalance affected the result of classification machine learning. The classification technique can divide the data into the given class target. This research focuses on four pre-processing methods: SMOTE, Spread Subsample, Class Balancer, and Resample. These methods can help to clean the data before undergoing the classification techniques. Resample shows the best result for solving the imbalance problem with 41.321 for Mean and Standard Deviation, 64.101. Besides, this research involves six classification techniques: Na�ve Bayes, BayesNet, Random Forest, Random Tree, Logistics, and Multilayer Perceptron. Indeed, the combination of Resample and Random Forest has the best result of Precision, 0.941, and ROC Area is 0.983. � 2022 IEEE. Final 2023-05-29T09:38:45Z 2023-05-29T09:38:45Z 2022 Conference Paper 10.1109/ICOCO56118.2022.10031922 2-s2.0-85148421899 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148421899&doi=10.1109%2fICOCO56118.2022.10031922&partnerID=40&md5=60d1e2d75b64921abbe14c1da66dae8f https://irepository.uniten.edu.my/handle/123456789/27021 316 323 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Balancing; Classification (of information); Digital storage; Machine learning; Random forests; And machine learning; Class imbalance; Classification performance; Classification technique; Detection performance; Machine-learning; Misclassifications; Random forests; Resamples; Research focus; Data mining
author2 57210731675
author_facet 57210731675
Zakaria A.Z.
Selamat A.
Cheng L.K.
Krejcar O.
format Conference Paper
author Zakaria A.Z.
Selamat A.
Cheng L.K.
Krejcar O.
spellingShingle Zakaria A.Z.
Selamat A.
Cheng L.K.
Krejcar O.
Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
author_sort Zakaria A.Z.
title Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
title_short Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
title_full Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
title_fullStr Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
title_full_unstemmed Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
title_sort improving class imbalance detection and classification performance: a new potential of combination resample and random forest
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425551479504896

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