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Classification with NormalBoost
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-1429-2345
2011 (English)In: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 20, no 2, 187-208 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a new boosting algorithm called NormalBoost which is capable of classifying a multi-dimensional binary class dataset. It adaptively combines several weak classifiers to form a strong classifier. Unlike many boosting algorithms which have high computation and memory complexities, NormalBoost is capable of classification with low complexity. Since NormalBoost assumes the dataset to be continuous, it is also noise resistant because it only deals with the means and standard deviations of each dimension. Experiments conducted to evaluate its performance shows that NormalBoost performs almost the same as AdaBoost in the classification rate. However, NormalBoost performs 189 times faster than AdaBoost and employs a very little amount of memory when a dataset of 2 million samples with 50 dimensions is invoked.

Place, publisher, year, edition, pages
London: de Gruyter Reference Global , 2011. Vol. 20, no 2, 187-208 p.
Keyword [en]
Pattern recognition, classification, binary classifiers, boosting.
National Category
Computer Systems
Research subject
Komplexa system - mikrodataanalys
Identifiers
URN: urn:nbn:se:du-5662DOI: 10.1515/jisys.2011.011OAI: oai:dalea.du.se:5662DiVA: diva2:520383
Available from: 2011-08-08 Created: 2011-08-08 Last updated: 2016-02-12Bibliographically approved

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