Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Parameter analysis of differential evolution based oversampling approach for highly imbalanced datasets
International Journal of Intelligent Systems and Applications in Engineering · Haziran 2021
YÖKSİS Kayıtları
Parameter Analysis of Differential Evolution Based Oversampling Approach for Highly Imbalanced Datasets
International Journal of Intelligent Systems and Applications in Engineering · 2021 TR DİZİN
DOÇENT MEHMET AKİF ŞAHMAN →
Makale Bilgileri
DergiInternational Journal of Intelligent Systems and Applications in Engineering
Yayın TarihiHaziran 2021
Cilt / Sayfa9 · 69-80
Scopus ID2-s2.0-85111092371
Erişim🔓 Açık Erişim
Özet
Nowadays, almost all performed activities are saved into databases. Data mining methods such as classifiers utilize these datasets for discovering hidden patterns and rules. Proposed methods for classification problems are generally developed considering approximately balanced datasets. However, imbalanced datasets that have unequal instance numbers in their classes emerge as a common problem in most real domains. Many approaches at the data level are proposed to enable better classification of imbalanced datasets. Differential Evolution Based Oversampling Approach For Highly Imbalanced Datasets (DEBOHID) is one of the proposed methods in order to handle this issue on imbalanced datasets. DEBOHID approach utilizes the crossover and mutation processes of DE for generating new synthetic samples. The parameters used by the crossover and mutation processes affect the solution quality. Therefore, in this study, solution quality in highly imbalanced datasets for different crossover and mutation parameter values of DEBOHID approach is investigated. Experimental studies are carried out by using three classifiers and two evaluation metrics for different parameter values. The obtained results are compared with well-known approaches in the literature.
Yazarlar (1)
1
Mehmet Akif Şahman
Anahtar Kelimeler
Class imbalance
Differential evolution
Imbalanced data learning
Oversampling
Parameter Analysis
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
3
Atıf
1
Yazar
5
Anahtar Kelime