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

Künye