A two-stage methodology for sequence classification based on sequential pattern mining and optimization
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Date
Authors
Exarchos, T. P.
Tsipouras, M. G.
Papaloukas, C.
Fotiadis, D. I.
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Elsevier
Abstract
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peer reviewed
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Data & Knowledge Engineering
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Description
We present a methodology for sequence classification, which employs sequential pattern mining and optimization, in a two-stage process. In the first stage, a sequence classification model is defined, based on a set of sequential patterns and two sets of weights are introduced, one for the patterns and one for classes. In the second stage, an optimization technique is employed to estimate the weight values and achieve optimal classification accuracy. Extensive evaluation of the methodology is carried out, by varying the number of sequences, the number of patterns and the number of classes and it is compared with similar sequence classification approaches. (c) 2008 Elsevier B.V. All rights reserved.
Description
Keywords
sequential pattern mining, sequential pattern matching, sequence classification, recognition, machines
Subject classification
Citation
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<Go to ISI>://000258801400007
http://ac.els-cdn.com/S0169023X08000748/1-s2.0-S0169023X08000748-main.pdf?_tid=099a97a427b1514744cf1439ea293940&acdnat=1339756685_f74d41c7a3f56360c3cc2f1991b6018a
http://ac.els-cdn.com/S0169023X08000748/1-s2.0-S0169023X08000748-main.pdf?_tid=099a97a427b1514744cf1439ea293940&acdnat=1339756685_f74d41c7a3f56360c3cc2f1991b6018a
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en
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Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών