Evolving fuzzy pattern trees for binary classification on data streams

Fuzzy pattern trees (FPTs) have recently been introduced as a novel model class for machine learning. In this paper, we consider the problem of learning fuzzy pattern trees for binary classification from data streams. Apart from its practical relevance, this problem is also interesting from a methodological point of view. First, the aspect of efficiency plays an important role in the context of data streams, since learning has to be accomplished under hard time (and memory) constraints. Moreover, a learning algorithm should be adaptive in the sense that an up-to-date model is offered at any time, taking new data items into consideration as soon as they arrive and perhaps forgetting old ones that have become obsolete due to a change of the underlying data generating process. To meet these requirements, we develop an evolving version of fuzzy pattern tree learning, in which model adaptation is realized by anticipating possible local changes of the current model, and confirming these changes through statistical hypothesis testing. In experimental studies, we compare our method to a state-of-the-art tree-based classifier for learning from data streams, showing that evolving pattern trees are competitive in terms of performance while typically producing smaller and more compact models.