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KDD Cup 2001: Gene/Protein Function Prediction Using the Multirelational Learning Algorithm RELAGGS Mark-A. Krogel Otto-von-Guericke-Universität, Magdeburg, Germany School of Computer Science, Research Group Stefan Wrobel Mark-A. Krogel, Magdeburg University, Knowledge Discovery and Machine Learning Group 1 Preparation: A Multirelational Task General: renormalize into multiple tables as a natural representation of the data genes_ relation gene class complex phenotype motif interactions_ relation interaction geneN Specific for KDD Cup tasks 2/3: consider only interactions with high correlations, assume transitivity, make symmetry explicit Mark-A. Krogel, Magdeburg University, Knowledge Discovery and Machine Learning Group 2 Algorithm: RELAGGS [Krogel/Wrobel:ILP01] Computes selected joins following user-defined foreign links Performs automatic transformation of multiple tables into single table with the help of aggregate functions gene class complex RELAGGS phenotype gene_ for_ analysis motif interaction geneN Uses propositional learner such as C4.5 or SVMlight Mark-A. Krogel, Magdeburg University, Knowledge Discovery and Machine Learning Group 3 Summary RELAGGS allows to work with natural multirelational form of data immediately Easy specification of possible joins with foreign links Maximal preservation of information through aggregation Accuracies: 93,6% on task 2: rank 1 69,8% on task 3: rank 4 http://kd.cs.uni-magdeburg.de M.-A. Krogel, S. Wrobel: Transformation-Based Learning Using Multirelational Aggregation. 11th International Conference On Inductive Logic Programming, Strasbourg, France, Sept. 2001. Mark-A. Krogel, Magdeburg University, Knowledge Discovery and Machine Learning Group 4