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Abstract
In recent years there has been an exponential increase in the amount of
publicly accessible biological information, such as DNA and protein
sequences. This has resulted in an increased interest in developing
computational techniques to automatically classify these large volumes of
sequence data into various categories corresponding to either their role
in the chromosomes, their structure, and/or their functions. This paper
evaluates some of the widely used sequence classification algorithms and
develops a framework for modeling sequences in a fashion so that
traditional machine learning algorithms, such as support vector machines,
can be applied easily. Experiments show that the SVM-based approaches are
able to achieve higher classification accuracy compared to the more
traditional sequence classification algorithms such as K-nearest neighbor
based approaches and Markov model based techniques.
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