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A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling

Applied Soft Computing · Mart 2025

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YÖKSİS Kayıtları
A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling
Applied Soft Computing · 2025 SCI-Expanded
Doç. Dr. AHMET CEVAHİR ÇINAR →
YÖKSİS ISSN Eşleşmesi

Bu dergide (ISSN eşleşmesi) kurumun 17 kaydı bulundu.

YÖKSİS Kayıtları — ISSN Eşleşmesi
Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models
2015 ISSN: 15684946 SCI-Expanded
Prof. Dr. HASAN ERDİNÇ KOÇER →
Color image segmentation based on multiobjective artificial bee colony optimization
2015 ISSN: 15684946 SCI-Expanded
Doç. Dr. TAHİR SAĞ →
Color image segmentation based on multiobjective artificial bee colony optimization
2015 ISSN: 15684946 SCI-Expanded
Prof. Dr. MEHMET ÇUNKAŞ →
Liver fibrosis staging using CT image texture analysis and soft computing
2014 ISSN: 15684946 SCI-Expanded
Prof. Dr. MEHMET ÖZTÜRK →
New Approaches to determine Age and Gender in Image Processing Techniques using Multilayer Perceptron Neural Network
2018 ISSN: 1568-4946 SCI-Expanded
Prof. Dr. FATİH BAŞÇİFTÇİ →
A modification of tree-seed algorithm using Deb’s rules for constrained optimization
2018 ISSN: 1568-4946 SCI Q1
Doç. Dr. AHMET CEVAHİR ÇINAR →
A hybrid binary grey wolf optimizer for selection and reduction of reference points with extreme learning machine approach on local GNSS/leveling geoid determination
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. KEMAL TÜTÜNCÜ →
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. AHMET CEVAHİR ÇINAR →
A discrete spotted hyena optimizer for solving distributed job shop scheduling problems
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
A hybrid binary grey wolf optimizer for selection and reduction of reference points with extreme learning machine approach on local GNSS/leveling geoid determination
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
2021 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. MEHMET AKİF ŞAHMAN →
Classification rule mining based on Pareto-based Multiobjective Optimization
2022 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. TAHİR SAĞ →
Classification rule mining based on Pareto-based Multiobjective Optimization
2022 ISSN: 1568-4946 SCI-Expanded Q1
Prof. Dr. HUMAR KAHRAMANLI ÖRNEK →
Image forgery detection by combining Visual Transformer with Variational Autoencoder Network
2024 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. ALİ YAŞAR →
Parametric picture fuzzy cross-entropy measures based on d-Choquet integral for building material recognition Applied Soft Computing
2024 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. DİLEK SÖYLEMEZ ÖZDEN →
A synergistic oversampling technique with differential evolution and safe level synthetic minority oversampling
2025 ISSN: 1568-4946 SCI-Expanded
Doç. Dr. AHMET CEVAHİR ÇINAR →
Efficiency analysis of binary metaheuristic optimization algorithms for uncapacitated facility location problems
2025 ISSN: 1568-4946 SCI-Expanded Q1
Doç. Dr. TAHİR SAĞ →

Makale Bilgileri

Dergi Applied Soft Computing
ISSN15684946
Yayın TarihiMart 2025
Cilt / Sayfa172
DOI10.1016/j.asoc.2025.112819
Scopus ID2-s2.0-85217671378
Özet Classification problems often face challenges when dealing with imbalanced datasets, leading to decreased performance. To address this issue, balancing the dataset becomes imperative for improved classification accuracy. Among various methods proposed in the literature, oversampling techniques are fundamental approaches to mitigating class imbalance. Synthetic Minority Over-sampling Technique (SMOTE) is a foundational technique in this domain. However, a more refined approach, Safe-Level-SMOTE, selectively utilizes crucial minority instances to generate synthetic samples. Another notable method, the Differential Evolution-Based Oversampling Approach for Highly Imbalanced Datasets (DEBOHID), leverages a differential evolution algorithm to handle highly imbalanced datasets effectively. This study presents a novel oversampling method (SL-D) that integrates Safe-Level-SMOTE with DEBOHID. SL-D offers three distinct variants: SL-D-Max, SL-D-Min, and SL-D-Mean, each tailored to specific scenarios. We introduce an adaptive calculation mechanism for the proposed method's crossover rate (CR) parameter. Our experimentation utilizes Decision Trees (DT), Support Vector Machines (SVM), and k-nearest neighbor (kNN) classifiers across forty-four highly imbalanced datasets. Results indicate that the SL-D-Max variant outperforms nine state-of-the-art oversampling approaches, as evidenced by superior performance metrics such as G-Mean and Area Under the Curve (AUC). Furthermore, statistical analysis employing the Friedman Test confirms the significant superiority of SL-D-Max. This study underscores the efficacy of the proposed hybrid oversampling technique in addressing imbalanced data classification challenges and highlights its potential for practical applications.

Yazarlar (1)

1
Ahmet Cevahir Cinar

Anahtar Kelimeler

Class imbalance Differential evolution Imbalanced learning Oversampling Safe-Level-SMOTE

Kurumlar

Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Applied Soft Computing
Q1
SJR Skoru1,511
H-Index208
YayıncıElsevier B.V.
ÜlkeNetherlands
Software (Q1)
Dergi sayfasına git

Metrikler

7
Atıf
1
Yazar
5
Anahtar Kelime

Sistemimizdeki Yazarlar

AÇ
AHMET CEVAHİR ÇINAR
Doç. Dr.

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