Change search
ReferencesLink to record
Permanent link

Direct link
Multiclass Adaboost Based on an Ensemble of Binary Adaboosts
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
University of Central Florida.
2013 (English)In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 3, no 2, 57-70 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a multi-class AdaBoost based on incorporating an ensemble of binary AdaBoosts which is organized as Binary Decision Tree (BDT). It is proved that binary AdaBoost is extremely successful in producing accurate classification but it does not perform very well for multi-class problems. To avoid this performance degradation, the multi-class problem is divided into a number of binary problems and binary AdaBoost classifiers are invoked to solve these classification problems. This approach is tested with a dataset consisting of 6500 binary images of traffic signs. Haar-like features of these images are computed and the multi-class AdaBoost classifier is invoked to classify them. A classification rate of 96.7% and 95.7% is achieved for the traffic sign boarders and pictograms, respectively. The proposed approach is also evaluated using a number of standard datasets such as Iris, Wine, Yeast, etc. The performance of the proposed BDT classifier is quite high as compared with the state of the art and it converges very fast to a solution which indicates it as a reliable classifier.

Place, publisher, year, edition, pages
USA: Scientific & Academic Publishing Co. , 2013. Vol. 3, no 2, 57-70 p.
Keyword [en]
Multiclass AdaBoost, Binary Decision Tree, Classification
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis
URN: urn:nbn:se:du-12801OAI: diva2:643016

Open Access

Available from: 2013-08-24 Created: 2013-08-24 Last updated: 2016-10-12Bibliographically approved

Open Access in DiVA

fulltext(952 kB)0 downloads
File information
File name FULLTEXT01.pdfFile size 952 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Fleyeh, Hasan
By organisation
Computer Engineering
In the same journal
American Journal of Intelligent Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 517 hits
ReferencesLink to record
Permanent link

Direct link