Fast Fuzzy Pattern Tree Learning

Fuzzy pattern trees have recently been introduced as a novel type of fuzzy system, specifically with regard to the modeling of classification functions in machine learning. Moreover, different algorithms for learning pattern trees from data have been proposed in the literature. While showing strong performance in terms of predictive accuracy, these algorithms exhibit a rather high computational complexity, and their runtime may become prohibitive for large data sets. In this paper, we therefore propose extensions of an existing state-of-the-art algorithm for fuzzy pattern tree induction, which are aimed at making this algorithm faster without compromising its predictive accuracy. These extensions include the use of adaptive sampling schemes as well as heuristics for guiding the growth of pattern trees. The effectiveness of our modified algorithm is confirmed by means of several experimental studies.