Soft decision trees

By Ozan Irsoy, Olcay Taner Yildiz, Ethem Alpaydin. 2012

150 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “In Optimal Randomized Classification and Regression Trees, the splitting rule at branch node t ∈ B is probabilistic (Irsoy et al. 2012; Yang et al. 2018), i.e., individuals move with a certain probability to the left child of t and with the complementary probability to the right one.”
Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020, background): “Soft Decision Trees (SDTs) have shown to be more accurate than traditional hard decision trees [23, 24, 46].”
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation (2018, background): “The way they design the soft decision tree is quite similar to [38].”
Recent advances in decision trees: an updated survey (2022, methodology): “…is determined only by the leaf with the highest probability (Frosst and Hinton 2017; Wan et al. 2020), whereas in the second way, the prediction is determined by an aggregation of all leaves, for example through a weighted sum (Suarez and Lutsko 1999; Irsoy et al. 2012, 2014; Hehn et al. 2020).”
Budding Trees (2014, methodology): “We use ten regression (ABAlone, ADD10, BOSton, CALifornia, COMp, CONcrete, puma8FH, puma8FM, puma8NH, puma8NM) and ten binary classification (BREast, GERman, MAGic, MUSk2, PIMa, POLyadenylation, RINgnorm, SATellite47, SPAmbase, TWOnorm) data sets from the UCI repository [11], as in [8].”
Adaptive Neural Trees (2018, methodology): “Architecture growth is a key facet of DTs (Criminisi & Shotton, 2013), and typically performed in a greedy fashion with a termination criteria based on validation set error (Su ´ arez & Lutsko, 1999; ˙Irsoy et al., 2012).”
Learning Binary Trees via Sparse Relaxation (2020, methodology): “For this reason, there has been a massive research eﬀort on how to learn such binary trees since the early days of machine learning (Breiman et al., 1984; İrsoy et al., 2012; Bertsimas & Dunn, 2017; Popov et al., 2019).”
Bayesian regression tree ensembles that adapt to smoothness and sparsity (2017, methodology): “The soft decision trees we use are similar in spirit to those used by Irsoy et al. (2012), who considered a soft variant of the CART algorithm.”