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Boosting the oversampling methods based on differential evolution strategies for imbalanced learning

Applied Soft Computing · Kasım 2021

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YÖKSİS Kayıtları
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
Applied Soft Computing · 2021 SCI-Expanded
DOÇENT AHMET CEVAHİR ÇINAR →
Boosting the oversampling methods based on differential evolution strategies for imbalanced learning
APPLIED SOFT COMPUTING · 2021 SCI-Expanded
DOÇENT MEHMET AKİF ŞAHMAN →

Makale Bilgileri

DergiApplied Soft Computing
Yayın TarihiKasım 2021
Cilt / Sayfa112
DOI10.1016/j.asoc.2021.107787
Scopus ID2-s2.0-85113352660
Özet The class imbalance problem is a challenging problem in the data mining area. To overcome the low classification performance related to imbalanced datasets, sampling strategies are used for balancing the datasets. Oversampling is a technique that increases the minority class samples in various proportions. In this work, these 16 different DE strategies are used for oversampling the imbalanced datasets for better classification. The main aim of this work is to determine the best strategy in terms of Area Under the receiver operating characteristic (ROC) Curve (AUC) and Geometric Mean (G-Mean) metrics. 44 imbalanced datasets are used in experiments. Support Vector Machines (SVM), k-Nearest Neighbor (kNN), and Decision Tree (DT) are used as a classifier in the experiments. The best results are produced by 6th Debohid Strategy (DSt6), 1th Debohid Strategy (DSt1), and 3th Debohid Strategy (DSt3) by using kNN, DT, and SVM classifiers, respectively. The obtained results outperform the 9 state-of-the-art oversampling methods in terms of AUC and G-Mean metrics

Yazarlar (4)

1
Sedat Korkmaz
2
Mehmet Akif Şahman
3
Ahmet Cevahir Cinar
4
Ersin Kaya

Anahtar Kelimeler

Class imbalance Differential evolution Differential evolution strategies Imbalanced datasets Imbalanced learning Oversampling

Kurumlar

Konya Technical University
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey

Metrikler

14
Atıf
4
Yazar
6
Anahtar Kelime

Sistemimizdeki Yazarlar

MŞ
MEHMET AKİF ŞAHMAN
DOÇENT

Hızlı Erişim

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