Enhanced ensemble-based classifier with boosting for pattern recognition

Eva Volna and Martin Kotyrba

Applied Mathematics and Computation, 2017, vol. 310, issue C, 1-14

Abstract: The aim of the article is a proposal of a classifier based on neural networks that will be applicable in machine digitization of incomplete and inaccurate data or data containing noise for the purpose of their classification (pattern recognition). The article is focused on the possibility of increasing the efficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time demands. Time demands do not mean runtime, nor its development, but time demands of applying the algorithm to a particular problem domain. In other words, the amount of professional labour that is needed for such an implementation. The article aims at methods from the field of pattern recognition, which primarily means various types of neural networks. The proposed approaches are verified experimentally.

Keywords: Neural networks; Classification; Boosting; Ensemble methods; Optimization (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:310:y:2017:i:c:p:1-14

DOI: 10.1016/j.amc.2017.04.019

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