Classification via ensembles of basic thresholding classifiers

Autor:	Mehmet Altan Toksöz, İlkay Ulusoy
Jazyk:	angličtina
Rok vydání:	2016
Předmět:	basic thresholding classifiers sparsity-based classification algorithms sufficient identification condition SIC threshold parameter selection fusion scheme Computer applications to medicine. Medical informatics R858-859.7 Computer software QA76.75-76.765
Zdroj:	IET Computer Vision, Vol 10, Iss 5, Pp 433-442 (2016)
Druh dokumentu:	article
ISSN:	1751-9640 1751-9632
DOI:	10.1049/iet-cvi.2015.0077
Popis:	The authors present a sparsity‐based algorithm, basic thresholding classifier (BTC), for classification applications which is capable of identifying test samples extremely rapidly and performing high classification accuracy. They introduce a sufficient identification condition (SIC) under which BTC can identify any test sample in the range space of a given dictionary. By using SIC, they develop a procedure which provides a guidance for the selection of threshold parameter. By exploiting rapid classification capability, they propose a fusion scheme in which individual BTC classifiers are combined to produce better classification results especially when very small number of features is used. Finally, they propose an efficient validation technique to reject invalid test samples. Numerical results in face identification domain show that BTC is a tempting alternative to sparsity‐based classification algorithms such as greedy orthogonal matching pursuit and l1‐minimisation.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/d96a14e05d3d49d8a853a2c45110270c Zobrazit plný text záznamu Plný text View record in DOAJ