Machine Learning Tools For Complex Data

This web page hosts machine learning algorithms developed for work with complex structured data in relational form. The data can, for example, describe large organic molecules such as proteins or groups of individuals such as social networks or predator-prey networks etc. Our algorithms are unique in that, in principle, they are able to search a given set of relational patterns exhaustively, thus guaranteeing that if some good pattern capturing an important feature of the problem exists, it will be found.


Our relational learning algorithms are tailored towards so-called tree-like features for which some otherwise very hard sub-problems (NP-hard) become tractable. The problem of finding a complete set of informative features remains hard also for tree-like features, however, we were able to develop algorithms for tree-like features which scale well for problems of real-life scale. Currently, our suite of machine learning algorithms integrates implementations of two relational learning algorithms HiFi, RelF and Poly. These algortihms can be accessed through a simple scripting interface or through an intuitive GUI which also allows the users to evaluate usefulness of the generated patterns when combined with several machine learning algorithms from WEKA.