Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/44107
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Title: Improving classification using a Confidence Matrix based on weak classifiers applied to OCR
Authors: Rico Juan, Juan Ramón | Calvo-Zaragoza, Jorge
Research Group/s: Reconocimiento de Formas e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Confidence Matrix | Posterior probability | Weak classifiers | Feature spaces
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 3-Mar-2015
Publisher: Elsevier
Citation: Neurocomputing. 2015, 151(3): 1354-1361. doi:10.1016/j.neucom.2014.10.058
Abstract: This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.
Sponsor: This work was partially supported by the Spanish CICyT through the project TIN2013-48152-C2-1-R, the Consejería de Educación de la Comunidad Valenciana through Project PROMETEO/2012/017 and a FPU fellowship (AP2012-0939) from the Spanish Ministerio de Educación Cultura y Deporte.
URI: http://hdl.handle.net/10045/44107
ISSN: 0925-2312 (Print) | 1872-8286 (Online)
DOI: 10.1016/j.neucom.2014.10.058
Language: eng
Type: info:eu-repo/semantics/article
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.neucom.2014.10.058
Appears in Collections:INV - GRFIA - Artículos de Revistas

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