DSpace
 

Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5379

Title: Fingerprint prediction using classifier ensembles
Authors: Molale, P
Twala, B
Seeletse, S
Keywords: Fingerprints
Classifier ensembles
Machine learning
Fingerprint prediction
Artificial neural network
SASA 2011
Issue Date: Nov-2011
Publisher: South African Statistical Association for 2011
Citation: Molale, P, Twala, B and Seeletse, S. 2011. Fingerprint prediction using classifier ensembles. 53rd Annual Conference of the South African Statistical Association for 2011 (SASA 2011), CSIR, CSIR Convention Centre, Pretoria, South Africa, 1-3 November 2011
Series/Report no.: Workflow request;7616
Abstract: In this study, the application of classifiers to problems in fingerprint prediction is investigated. Six supervised learning and two statistical fingerprint classification methods (classifiers) are considered: linear discriminant analysis (LDA); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems are also demonstrated and evaluated in terms of their ability to correctly predicting or classifying a fingerprint using the National Institute of Standards and Technology (NIST) biometric image database. Examining the performance of the base classifiers showed DT, SVM and ANN to have the highest accuracy while LDA has the lowest accuracy rate. The results further show all the multi stage systems to significantly outperform the baseline classifiers. Accordingly, good performance is consistently derived from boosting.
Description: 53rd Annual Conference of the South African Statistical Association for 2011 (SASA 2011), CSIR, CSIR Convention Centre, Pretoria, South Africa, 1-3 November 2011
URI: http://hdl.handle.net/10204/5379
Appears in Collections:Advanced mathematical modelling and simulation
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Molale_2011.pdf320.55 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback