dc.contributor.author |
Molale, P
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|
dc.contributor.author |
Twala, B
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|
dc.contributor.author |
Seeletse, S
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dc.date.accessioned |
2011-12-09T11:36:58Z |
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dc.date.available |
2011-12-09T11:36:58Z |
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dc.date.issued |
2011-11 |
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dc.identifier.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 |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/5379
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dc.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 |
en_US |
dc.description.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. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
South African Statistical Association for 2011 |
en_US |
dc.relation.ispartofseries |
Workflow request;7616 |
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dc.subject |
Fingerprints |
en_US |
dc.subject |
Classifier ensembles |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Fingerprint prediction |
en_US |
dc.subject |
Artificial neural network |
en_US |
dc.subject |
SASA 2011 |
en_US |
dc.title |
Fingerprint prediction using classifier ensembles |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Molale, P., Twala, B., & Seeletse, S. (2011). Fingerprint prediction using classifier ensembles. South African Statistical Association for 2011. http://hdl.handle.net/10204/5379 |
en_ZA |
dc.identifier.chicagocitation |
Molale, P, B Twala, and S Seeletse. "Fingerprint prediction using classifier ensembles." (2011): http://hdl.handle.net/10204/5379 |
en_ZA |
dc.identifier.vancouvercitation |
Molale P, Twala B, Seeletse S, Fingerprint prediction using classifier ensembles; South African Statistical Association for 2011; 2011. http://hdl.handle.net/10204/5379 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Molale, P
AU - Twala, B
AU - Seeletse, S
AB - 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.
DA - 2011-11
DB - ResearchSpace
DP - CSIR
KW - Fingerprints
KW - Classifier ensembles
KW - Machine learning
KW - Fingerprint prediction
KW - Artificial neural network
KW - SASA 2011
LK - https://researchspace.csir.co.za
PY - 2011
T1 - Fingerprint prediction using classifier ensembles
TI - Fingerprint prediction using classifier ensembles
UR - http://hdl.handle.net/10204/5379
ER -
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en_ZA |