A Discriminative Dictionary Learning-AdaBoost-SVM Classification Method on Imbalanced Datasets
Küçük Resim Yok
Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Sparse representation is a signal processing method which is mostly used in signal compression, noise reduction, and signal and image restoration fields. In this study, sparse representation was used in a different way from the traditional methods. In the proposed method, a hybrid structure was created by combining dictionary learning and ensemble classifier AdaBoost algorithms. The main idea of this method is to obtain the sparse coefficients from an over-complete dictionary and to use the coefficients in the weight update formula of AdaBoost. Support Vector Machines (SVM) classifier was used as weak classifiers of AdaBoost, and AdaBoost-SVM classifier structure was created. Multiplying the sparse coefficients with weight of weak learners process in weight update formula has given satisfying results on imbalanced datasets during the experiments.
Açıklama
2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
Anahtar Kelimeler
dictionary learning, ensemble classifiers, sparse representation, weak classifiers, weight update
Kaynak
2017 INTERNATIONAL ARTIFICIAL INTELLIGENCE AND DATA PROCESSING SYMPOSIUM (IDAP)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
