Budding Trees

By Ozan Irsoy, Olcay Taner Yildiz, Ethem Alpaydin. 2014

23 citations recorded by the source corpus.

We propose a new decision tree model, named the budding tree, where a node can be both a leaf and an internal decision node. Each bud node starts as a leaf node, can then grow children, but then later on, if necessary, its children can be pruned. This contrasts with traditional tree construction algorithms that only grows the tree during the training phase, and prunes it in a separate pruning phase. We use a soft tree architecture and show that the tree and its parameters can be trained using gradient-descent. Our experimental results on regression, binary classification, and multi-class classification data sets indicate that our newly proposed model has better performance than traditional trees in terms of accuracy while inducing trees of comparable size.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Recent advances in decision trees: an updated survey (2022, methodology): “Budding Trees (Irsoy et al. 2014) eschew both greedy induction and fine-tuning by considering every node to be both a leaf and an internal node, therefore conflating tree construction with pruning and updating all weights at every iteration.”
Adaptive Neural Trees (2018, background): “…(Su´arez & Lutsko, 1999) SDT 2 / HME (Jordan & Jacobs, 1994) SDT 3 (˙Irsoy et al., 2012) SDT 4 (Frosst & Hinton, 2017) RDT (L´eon & Denoyer, 2015) BT (˙Irsoy et al., 2014) Conv DT (Laptev & Buhmann, 2014) NDT (Rota Bulo & Kontschieder, 2014) NDT 2 (Xiao, 2017) NDF (Kontschieder et al., 2015) CNet…”