Abstraction Augmented Markov Models

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Article discussing the abstraction augmented Markov models.

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10 p.: ill.

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Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina & Honavar, Vasant December 2010.


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Article discussing the abstraction augmented Markov models.

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10 p.: ill.


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Abstract: High accuracy sequence classification often requires the use of higher order Markov models (MMs). However, the number of MM parameters increases exponentially with the range of direct dependencies between sequence elements, thereby increasing the risk of overfitting when the data set is limited in size. We present abstraction augmented Markov models (AAMMs) that effectively reduce the number of numeric parameters of kᵗʰ order MMs by successively grouping strings of length k (i.e., k-grams) into abstraction hierarchies. We evaluate AAMMs on three protein subcellular localization prediction tasks. The results of our experiments show that abstraction makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) as compared to MMs. AAMMs are competitive with and, in some cases, significantly outperform MMs. Moreover, the results show that AAMMs often perform significantly better than variable order Markov models, such as decomposed context tree weighting, prediction by partial match, and probabilistic suffix trees.


  • Proceedings of the Tenth Institute of Electrical and Electronics Engineers (IEEE) International Conference on Data Mining, 2010, Sydney, Australia


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  • December 2010

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  • Sept. 6, 2013, 3:22 p.m.

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  • March 27, 2014, 11:20 a.m.

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Caragea, Cornelia; Silvescu, Adrian; Caragea, Doina & Honavar, Vasant. Abstraction Augmented Markov Models, paper, December 2010; [New York, New York]. (digital.library.unt.edu/ark:/67531/metadc180962/: accessed February 16, 2019), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.