NDLI: Combining Super-Structuring and Abstraction on Sequence Classification

Content Provider	IEEE Xplore Digital Library
Author	Silvescu, A. Caragea, C. Honavar, V.
Copyright Year	2009
Abstract	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.
Starting Page	986
Ending Page	991
File Size	335997
Page Count	6
File Format	PDF
ISBN	9781424452422
ISSN	15504786
DOI	10.1109/ICDM.2009.130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Predictive models Proteins Accuracy Biological system modeling Computer science Data mining Computational biology Amino acids Diversity reception feature selection super-structuring abstraction
Content Type	Text
Resource Type	Article

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