Combining super-structuring and abstraction on sequence classification

Adrian Silvescu, Cornelia Caragea, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


We present an approach to adapting the data representation used by a learner on sequence classification tasks. Our approach that exploits the complementary strengths of super-structuring (constructing complex features by combining existing features) and abstraction (grouping of similar features to generate more abstract features), yields smaller and, at the same time, accurate models. Super-structuring provides a way to increase the predictive accuracy of the learned models by enriching the data representation (and hence, increases the complexity of the learned models) whereas abstraction helps reduce the number of model parameters by simplifying the data representation. The results of our experiments on two data sets drawn from macromolecular sequence classification applications show that adapting data representation by combining super-structuring and abstraction, makes it possible to construct predictive models that use significantly smaller number of features (by one to three orders of magnitude) than those that are obtained using super-structuring alone, without sacrificing predictive accuracy. Our experiments also show that simplifying data representation using abstraction yields better performing models than those obtained using feature selection.

Original languageEnglish (US)
Title of host publicationICDM 2009 - The 9th IEEE International Conference on Data Mining
Number of pages6
StatePublished - 2009
Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 9 2009

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other9th IEEE International Conference on Data Mining, ICDM 2009
Country/TerritoryUnited States
CityMiami, FL

All Science Journal Classification (ASJC) codes

  • General Engineering


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