Fuzzy pattern tree induction was recently introduced as a novel machine learning method for classification. Roughly speaking, a pattern tree is a hierarchical, tree-like structure, whose inner nodes are marked with generalized (fuzzy) logical operators and whose leaf nodes are associated with fuzzy predicates on input attributes. A pattern tree classifier is composed of an ensemble of such pattern trees, one for each class label. This type of classifier is interesting for several reasons. For example, since a single pattern tree can be considered as a kind of logical description of a class, it is quite appealing from an interpretation point of view. Moreover, in terms of classification accuracy, the method has shown promising performance in first experimental studies.
Maschine Learning Methods for Fuzzy Pattern Tree Induction Robin Senge, PhD-Thesis, University of Marburg, 2014 http://archiv.ub.uni-marburg.de/diss/z2014/0391/pdf/drs.pdf
Top-down induction of fuzzy pattern trees Robin Senge, Eyke Hüllermeier - IEEE Transactions on Fuzzy Systems, 2011
Evolving fuzzy pattern trees for binary classification on data streams Ammar Shaker, Robin Senge, Eyke Hüllermeier - Information Sciences, 2013
Pattern trees for regression and fuzzy systems modeling Robin Senge, Eyke Hüllermeier - 2010 IEEE International Cnoference on Fuzzy Systems, 2010
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