A Discriminative Dictionary Learning-AdaBoost-SVM Classification Method on Imbalanced Datasets

Barstugan, MucahidCeylan, Rahime2020-03-262020-03-262017978-1-5386-1880-6https://hdl.handle.net/20.500.12395/347182017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYSparse 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.eninfo:eu-repo/semantics/closedAccessdictionary learningensemble classifierssparse representationweak classifiersweight updateA Discriminative Dictionary Learning-AdaBoost-SVM Classification Method on Imbalanced DatasetsConference ObjectN/AWOS:000426868700177N/A