Embedding Decision Trees and Random Forests in Constraint Programming

2015 ◽  
pp. 74-90 ◽  
Author(s):  
Alessio Bonfietti ◽  
Michele Lombardi ◽  
Michela Milano
Keyword(s):  
Decision Trees ◽  
Random Forests ◽  
Constraint Programming

scholarly journals Predictors of progression through the cascade of care to a cure for hepatitis C patients using decision trees and random forests

2021 ◽  
pp. 104461
Author(s):  
Jasmine Ye Nakayama ◽  
Joyce Ho ◽  
Emily Cartwright ◽  
Roy Simpson ◽  
Vicki Stover Hertzberg
Keyword(s):  
Hepatitis C ◽  
Decision Trees ◽  
Random Forests ◽  
Cascade Of Care

scholarly journals Learning Optimal Decision Trees using Constraint Programming (Extended Abstract)

2020 ◽  
Author(s):  
Hélène Verhaeghe ◽  
Siegfried Nijssen ◽  
Gilles Pesant ◽  
Claude-Guy Quimper ◽  
Pierre Schaus
Keyword(s):  
Decision Trees ◽  
Constraint Programming ◽  
Greedy Algorithms ◽  
Building Blocks ◽  
Optimal Decision ◽  
Programming Approach ◽  
Learning Problem ◽  
New Approach ◽  
Good Classification ◽  
Additional Constraints

Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms have their disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, and it is hard to impose additional constraints on the models that are learned. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. In this paper, we introduce a new approach to learn decision trees using constraint programming. Compared to earlier approaches, we show that our approach obtains better performance, while still being sufficiently flexible to allow for the inclusion of constraints. Our approach builds on three key building blocks: (1) the use of AND/OR search, (2) the use of caching, (3) the use of the CoverSize global constraint proposed recently for the problem of itemset mining. This allows our constraint programming approach to deal in a much more efficient way with the decompositions in the learning problem.

Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees

2015 ◽  
Vol 82 (2) ◽  
pp. 187-196 ◽  
Author(s):  
Tuo Zhao ◽  
Yunxin Zhao ◽  
Xin Chen
Keyword(s):  
Decision Trees ◽  
Random Forests ◽  
Acoustic Modeling

Load-Capacity Rating of Bridge Populations through Machine Learning: Application of Decision Trees and Random Forests

2017 ◽  
Vol 22 (10) ◽  
pp. 04017076 ◽  
Author(s):  
Mohamad Alipour ◽  
Devin K. Harris ◽  
Laura E. Barnes ◽  
Osman E. Ozbulut ◽  
Julia Carroll
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Random Forests ◽  
Load Capacity ◽  
Capacity Rating

Learning optimal decision trees using constraint programming

Constraints ◽  
2020 ◽  
Vol 25 (3-4) ◽  
pp. 226-250
Author(s):  
Hélène Verhaeghe ◽  
Siegfried Nijssen ◽  
Gilles Pesant ◽  
Claude-Guy Quimper ◽  
Pierre Schaus
Keyword(s):  
Decision Trees ◽  
Constraint Programming ◽  
Optimal Decision

scholarly journals Privately Evaluating Decision Trees and Random Forests

2016 ◽  
Vol 2016 (4) ◽  
pp. 335-355 ◽  
Author(s):  
David J. Wu ◽  
Tony Feng ◽  
Michael Naehrig ◽  
Kristin Lauter
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Random Forests ◽  
Feature Vector ◽  
Tree Evaluation

Abstract Decision trees and random forests are common classifiers with widespread use. In this paper, we develop two protocols for privately evaluating decision trees and random forests. We operate in the standard two-party setting where the server holds a model (either a tree or a forest), and the client holds an input (a feature vector). At the conclusion of the protocol, the client learns only the model’s output on its input and a few generic parameters concerning the model; the server learns nothing. The first protocol we develop provides security against semi-honest adversaries. We then give an extension of the semi-honest protocol that is robust against malicious adversaries. We implement both protocols and show that both variants are able to process trees with several hundred decision nodes in just a few seconds and a modest amount of bandwidth. Compared to previous semi-honest protocols for private decision tree evaluation, we demonstrate a tenfold improvement in computation and bandwidth.

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