Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
379158 | Data & Knowledge Engineering | 2008 | 21 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Themis P. Exarchos, Markos G. Tsipouras, Costas Papaloukas, Dimitrios I. Fotiadis,