Learning to Classify Structured Data by Graph Propositionalization
2006 (English)In: Proceedings of the Second IASTED International Conference on Computational Intelligence, 2006Conference paper (Refereed)
Existing methods for learning from structured data are limited with respect to handling large or isolated substructures and also impose constraints on search depth and induced structure length. An approach to learning from structured data using a graph based propositionalization method, called finger printing, is introduced that addresses the limitations of current methods. The method is implemented in a system called DIFFER, which is demonstrated to compare favorable to existing state-of-art methods on some benchmark data sets. It is shown that further improvements can be obtained by combining the features generated by finger printing with features generated by previous methods.
Place, publisher, year, edition, pages
Machine Learning, Graph, Classification, Structured data
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-38408OAI: oai:DiVA.org:su-38408DiVA: diva2:310286
The IASTED International Conference on Computational Intelligence, November 20 – 22, 2006, San Francisco, USA