Relational information gain

Abstract. Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain and a discriminant function. In predicting the metal bonding state of proteins, TET achieve significant improvements over manually curated motifs, and the expressiveness of combinatorial features significantly contributes to such performance. Preliminary collective classification results seem to indicate it as a promising direction for further research. 1 Learning Type Extension Trees A TET [1] consists of a tree-structured logic formula where nodes are conjunctions of literals, and edges are labeled with sets of variables. Instead of a simple truth assignment, a TET defines a complex combinatorial feature whose recursive value structure accounts for the number of times each subtree can be ...

• Type: Journal
• Year: 2011
• Where: ML
• Authors: Marco Lippi, Manfred Jaeger, Paolo Frasconi, Andrea Passerini
• Keywords: Combinatorial Feature, Complex Combinatorial Feature, Machine Learning, ML 2011, Type Extension Trees