| dc.creator | MIGUEL ANGEL PALACIOS ALONSO | |
| dc.creator | CARLOS ALBERTO BRIZUELA RODRIGUEZ | |
| dc.creator | LUIS ENRIQUE SUCAR SUCCAR | |
| dc.date | 2009 | |
| dc.date.accessioned | 2023-07-25T16:23:12Z | |
| dc.date.available | 2023-07-25T16:23:12Z | |
| dc.identifier | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1202 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7806400 | |
| dc.description | Many problems such as voice recognition, speech recognition and many other tasks have been tackled with Hidden Markov Models (HMMs). These problems can also be dealt with an extension of the Naive Bayesian Classifier (NBC) known as Dynamic NBC (DNBC). From a dynamic Bayesian network (DBN) perspective, in a DNBC at each time there is a NBC. NBCs work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelevant attributes which are dependent of some relevant ones. Therefore, to increase this classifier accuracy, we need a method to design network structures that can capture the dependencies and get rid of irrelevant attributes. Furthermore, when we deal with dynamical processes there are temporal relations that should be considered in the network design. In order to learn automatically these models from data and increase the classifier accuracy we propose an evolutionary optimization algorithm to solve this design problem. We introduce a new encoding scheme and new genetic operators which are natural extensions of previously proposed encoding and operators for grouping problems. The design methodology is applied to solve the recognition problem for nine hand gestures. Experimental results show that the evolved network has higher average classification accuracy than the basic DNBC and a HMM. | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | Springer Science + Business Media | |
| dc.relation | citation:Palacios-Alonso, M.A., et al., (2009). Evolutionary learning of dynamic naive bayesian classifiers, Journal of Automated Reasoning, (45): 21–37 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0 | |
| dc.subject | info:eu-repo/classification/Naive Bayes classifier/Naive Bayes classifier | |
| dc.subject | info:eu-repo/classification/Dynamic Bayesian networks/Dynamic Bayesian networks | |
| dc.subject | info:eu-repo/classification/Genetic algorithms/Genetic algorithms | |
| dc.subject | info:eu-repo/classification/Gesture recognition/Gesture recognition | |
| dc.subject | info:eu-repo/classification/cti/1 | |
| dc.subject | info:eu-repo/classification/cti/12 | |
| dc.subject | info:eu-repo/classification/cti/1203 | |
| dc.subject | info:eu-repo/classification/cti/1203 | |
| dc.title | Evolutionary learning of dynamic naive bayesian classifiers | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/acceptedVersion | |
| dc.audience | students | |
| dc.audience | researchers | |
| dc.audience | generalPublic | |
