'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
We discuss a novel decision tree architecture with soft decisions at the internal nodes where we choose both children with probabilities given by a sigmoid gating function. Our algorithm is incremental where new nodes are added when needed and parameters are learned using gradient-descent. We visualize the soft tree fit on a toy data set and then compare it with the canonical, hard decision tree over ten regression and classification data sets. Our proposed model has significantly higher accuracy using fewer nodes.This work is supported by TUBITAK 109E186 and Bogazici University Scientific Research Project BAP5701.Publisher's Versio
