Deep Fuzzy Envelope Sample Generation Mechanism for Imbalanced Ensemble Classification
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Ensemble methods are widely used to tackle class imbalance problem. However, for existing imbalanced ensemble (IE) methods, the samples in each subset are resampled from the same dataset, and are directly input to the classifier for training, so the quality (diversity and separability) of the subsets is unsatisfactory usually. To solve the problem, a deep fuzzy envelope sample generation mechanism is proposed. First, the fuzzy C-means clustering based deep sample envelope prenetwork (DSEN) is designed to mine correlation information among samples, thereby increasing the quality of the subsets. Second, the local manifold structure metric and global structure distribution metric are designed to construct local-global structure consistency mechanism (LGSCM) to enhance distribution consistency of interlayer samples of DSEN. Third, the DSEN and LGSCM are combined to form the final deep sample envelope network-DSENLG to refresh the existing subsets. Finally, base classifiers are applied on the new subsets generated by the DSENLG and then fused, thereby realizing a new IE algorithm. The experimental results show that the proposed algorithm is significantly better than existing representative IE algorithms and it achieves the highest improvement of 10.64%, 19.5%, 18.67% and 22.33% on four criteria over the state-of-the-art methods. The originality of the article is threefold: proposing the concept of deep fuzzy samples or envelope samples, which comprehensively considers the correlation information among original samples; proposing the LGSCM to resolve the distribution inconsistency of interlayer samples; and forming an fuzzy envelope sample based IE algorithm.
Açıklama
Anahtar Kelimeler
Ensemble Learning, Correlation, Classification Algorithms, Training, Manifolds, Measurement, Clustering Algorithms, Class Imbalance Problem, Domain Adaption, Ensemble Approach, Envelope Learning, Fuzzy C-Means (Fcms) Clustering
Kaynak
Ieee Transactions on Fuzzy Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
32
Sayı
3
