A Toolbox for Learning from Relational Data with Propositional and Multi-instance Learners
Most databases employ the relational model for data storage. To use this data in a propositional learner, a propositionalization step has to take place. Similarly, the data has to be transformed to be amenable to a multi-instance learner. The Proper Toolbox contains an extended version of RELAGGS, the Multi-Instance Learning Kit MILK, and can also combine the multi-instance data with aggregated data from RELAGGS. RELAGGS was extended to handle arbitrarily nested relations and to work with both primary keys and indices. For MILK the relational model is flattened into a single table and this data is fed into a multi-instance learner. REMILK finally combines the aggregated data produced by RELAGGS and the multi-instance data, flattened for MILK, into a single table that is once again the input for a multi-instance learner. Several well-known datasets are used for experiments which highlight the strengths and weaknesses of the different approaches.
KeywordsInductive Logic Programming Nominal Attribute Decision Tree Learner Single Table Target Table
Unable to display preview. Download preview PDF.
- 1.Frank, E., Xu, X.: Applying Propositional Learning Algorithms to Multi-instance data. Working Paper 06/03, Computer Science, University of Waikato (2003)Google Scholar
- 2.Krogel, M.-A., Wrobel, S.: Facets of Aggregation Approaches to Propositionalization. In: Horváth, T., Yamamoto, A. (eds.) Proceedings of the Work-in-Progress Track at the 13th International Conference on Inductive Logic Programming (2003)Google Scholar
- 4.Reutemann, P.: Development of a Propositionalization Toolbox. MSc Thesis, Computer Science, University of Freiburg (2004)Google Scholar