A Feature Generation Algorithm for Sequences with Application to Splice-Site Prediction

  • Rezarta Islamaj
  • Lise Getoor
  • W. John Wilbur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


In this paper we present a new approach to feature selection for sequence data. We identify general feature categories and give construction algorithms for each of them. We show how they can be integrated in a system that tightly couples feature construction and feature selection. This integrated process, which we refer to as feature generation, allows us to systematically search a large space of potential features. We demonstrate the effectiveness of our approach for an important component of the gene finding problem, splice-site prediction. We show that predictive models built using our feature generation algorithm achieve a significant improvement in accuracy over existing, state-of-the-art approaches.


Feature Selection False Positive Rate Splice Site Information Gain Feature Selection Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rezarta Islamaj
    • 1
  • Lise Getoor
    • 1
  • W. John Wilbur
    • 2
  1. 1.Computer Science DepartmentUniversity of MarylandCollege ParkUSA
  2. 2.National Center for Biotechnology Information, NLM, NIHBethesdaUSA

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