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Classification and prediction by decision trees and neural networks

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dc.title Classification and prediction by decision trees and neural networks en
dc.contributor.author Procházka, Michal
dc.contributor.author Kouřil, Lukáš
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof MENDEL 2009
dc.identifier.issn 1803-3814 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.identifier.isbn 9788021438842
dc.date.issued 2009
dc.citation.spage 177
dc.citation.epage 181
dc.event.title 15th International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Fuzzy Logic, Rough Sets, Neural Networks, Fractals, Bayesian Methods, MENDEL 2009
dc.event.location Brno
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2009-06-24
dc.event.edate 2009-06-26
dc.type conferenceObject
dc.language.iso en
dc.publisher Brno University of Technology
dc.subject Classification en
dc.subject Data mining en
dc.subject Decision trees en
dc.subject Id3 en
dc.subject J48 en
dc.subject Mathematica. en
dc.subject Neural networks en
dc.subject Prediction en
dc.description.abstract In this paper we present comparative study of two frequently used methods for prediction and classification in data mining. These methods are decision trees and neural networks. Decision trees with J48 and ID3 algorithms are used to solve common classification problems where the data sets have several non-category attributes and one category attribute. In this case we need to predict category attribute which depends on the others. Neural networks have wide utilization including function approximation, data processing, prediction, classification etc. Technical neural networks offer profoundly different approach to solve problems in comparison with other methods. It could be very interesting to compare these dissimilar methods in terms of efficiency, execution speed and error rates. We demonstrate results of this comparison. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004930
utb.identifier.scopus 2-s2.0-84907936275
utb.identifier.wok 000273029500027
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:56:07Z
dc.date.available 2015-06-04T12:56:07Z
utb.contributor.internalauthor Procházka, Michal
utb.contributor.internalauthor Kouřil, Lukáš
utb.contributor.internalauthor Zelinka, Ivan
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