Oversampling Methods for Handling Imbalance Data in Binary Classification

Data preparation occupies the majority of data science, about 60–80%. The process of data preparation can produce an accurate output of information to be used in decision making. That is why, in the context of data science, it is so critical. However, in reality, data does not always come in a prede...

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Published in:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Main Author: Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
Format: Conference paper
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168762818&doi=10.1007%2f978-3-031-37108-0_1&partnerID=40&md5=da50f78b048c1911c8044b45cdb4bf93
id 2-s2.0-85168762818
spelling 2-s2.0-85168762818
Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
Oversampling Methods for Handling Imbalance Data in Binary Classification
2023
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14105 LNCS

10.1007/978-3-031-37108-0_1
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168762818&doi=10.1007%2f978-3-031-37108-0_1&partnerID=40&md5=da50f78b048c1911c8044b45cdb4bf93
Data preparation occupies the majority of data science, about 60–80%. The process of data preparation can produce an accurate output of information to be used in decision making. That is why, in the context of data science, it is so critical. However, in reality, data does not always come in a predefined distribution with parameters, and it can even arrive with an imbalance. Imbalanced data generates a lot of problems, especially in classification. This study employs several oversampling methods in machine learning, i.e., Random Oversampling (ROS), Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-sampling Technique (SMOTE), and Borderline-SMOTE (B-SMOTE), to handle imbalanced data in binary classification with Naïve Bayes and Support Vector Machine (SVM). The five methods will be run in the same experimental design and discussed in search of the best and most accurate model for the datasets. The evaluation was assessed based on the confusion matrices with precision, recall, and F1-score calculated for comparison. The AUC and ROC curve is also provided to evaluate the performance of each method via figures. The proposed work reveals that SVM with B-SMOTE has better classification performance, especially in datasets with high similarity characteristics between the minority and majority classes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Springer Science and Business Media Deutschland GmbH
3029743
English
Conference paper

author Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
spellingShingle Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
Oversampling Methods for Handling Imbalance Data in Binary Classification
author_facet Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
author_sort Riston T.; Suherman S.N.; Yonnatan Y.; Indrayatna F.; Pravitasari A.A.; Sari E.N.; Herawan T.
title Oversampling Methods for Handling Imbalance Data in Binary Classification
title_short Oversampling Methods for Handling Imbalance Data in Binary Classification
title_full Oversampling Methods for Handling Imbalance Data in Binary Classification
title_fullStr Oversampling Methods for Handling Imbalance Data in Binary Classification
title_full_unstemmed Oversampling Methods for Handling Imbalance Data in Binary Classification
title_sort Oversampling Methods for Handling Imbalance Data in Binary Classification
publishDate 2023
container_title Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
container_volume 14105 LNCS
container_issue
doi_str_mv 10.1007/978-3-031-37108-0_1
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168762818&doi=10.1007%2f978-3-031-37108-0_1&partnerID=40&md5=da50f78b048c1911c8044b45cdb4bf93
description Data preparation occupies the majority of data science, about 60–80%. The process of data preparation can produce an accurate output of information to be used in decision making. That is why, in the context of data science, it is so critical. However, in reality, data does not always come in a predefined distribution with parameters, and it can even arrive with an imbalance. Imbalanced data generates a lot of problems, especially in classification. This study employs several oversampling methods in machine learning, i.e., Random Oversampling (ROS), Adaptive Synthetic Sampling (ADASYN), Synthetic Minority Over-sampling Technique (SMOTE), and Borderline-SMOTE (B-SMOTE), to handle imbalanced data in binary classification with Naïve Bayes and Support Vector Machine (SVM). The five methods will be run in the same experimental design and discussed in search of the best and most accurate model for the datasets. The evaluation was assessed based on the confusion matrices with precision, recall, and F1-score calculated for comparison. The AUC and ROC curve is also provided to evaluate the performance of each method via figures. The proposed work reveals that SVM with B-SMOTE has better classification performance, especially in datasets with high similarity characteristics between the minority and majority classes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publisher Springer Science and Business Media Deutschland GmbH
issn 3029743
language English
format Conference paper
accesstype
record_format scopus
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