We present ongoing research on applying statistical relational learning techniques, in particular, propositionalization, to the challenging and interesting real-world domain of functional gene classi.cation of the Yeast genome Sachharomyces Cerevisiae. The main objective of this paper is to identify and describe the structural and statistical properties of this domain and examine how they conflict with the assumptions of the traditional relational learning approaches. Such properties are, in fact, shared by many relational application domains and potential solutions will be of interest far beyond the particular genetic application. We also report some preliminary experimental results on potential solutions for overcoming the limitations of our modeling approach by extending the existing automated feature construction strategies to accommodate the specific domain properties.
By: Claudia Perlich; Srujana Merugu
Published in: RC23678 in 2005
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .