On identifying academic homepages for digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Discovering health-related knowledge in social media using ensembles of heterogeneous features
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
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.