Learning optimal decision trees using constraint programming
Decision trees are among the most popular classification models in machine learning.
Traditionally, they are learned using greedy algorithms. However, such algorithms pose …
Traditionally, they are learned using greedy algorithms. However, such algorithms pose …
Compact-table: efficiently filtering table constraints with reversible sparse bit-sets
J Demeulenaere, R Hartert, C Lecoutre… - Principles and Practice …, 2016 - Springer
In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce Generalized
Arc Consistency (GAC) on table constraints. Although this algorithm is the default propagator …
Arc Consistency (GAC) on table constraints. Although this algorithm is the default propagator …
XCSP3: an integrated format for benchmarking combinatorial constrained problems
F Boussemart, C Lecoutre, G Audemard… - arXiv preprint arXiv …, 2016 - arxiv.org
We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated
representations of combinatorial constrained problems. This new format is able to deal with …
representations of combinatorial constrained problems. This new format is able to deal with …
Seapearl: A constraint programming solver guided by reinforcement learning
The design of efficient and generic algorithms for solving combinatorial optimization
problems has been an active field of research for many years. Standard exact solving …
problems has been an active field of research for many years. Standard exact solving …
PYCSP3: modeling combinatorial constrained problems in python
C Lecoutre, N Szczepanski - arXiv preprint arXiv:2009.00326, 2020 - arxiv.org
In this document, we introduce PyCSP $3 $, a Python library that allows us to write models of
combinatorial constrained problems in a declarative manner. Currently, with PyCSP $3 …
combinatorial constrained problems in a declarative manner. Currently, with PyCSP $3 …
Modelling diversity of solutions
For many combinatorial problems, finding a single solution is not enough. This is clearly the
case for multi-objective optimization problems, as they have no single “best solution” and …
case for multi-objective optimization problems, as they have no single “best solution” and …
SAT-based approach for learning optimal decision trees with non-binary features
Decision trees are a popular classification model in machine learning due to their
interpretability and performance. Traditionally, decision-tree classifiers are constructed using …
interpretability and performance. Traditionally, decision-tree classifiers are constructed using …
Conflict ordering search for scheduling problems
We introduce a new generic scheme to guide backtrack search, called Conflict Ordering
Search (COS), that reorders variables on the basis of conflicts that happen during search …
Search (COS), that reorders variables on the basis of conflicts that happen during search …
Coversize: A global constraint for frequency-based itemset mining
Constraint Programming is becoming competitive for solving certain data-mining problems
largely due to the development of global constraints. We introduce the CoverSize constraint …
largely due to the development of global constraints. We introduce the CoverSize constraint …
Xcsp3-core: A format for representing constraint satisfaction/optimization problems
F Boussemart, C Lecoutre, G Audemard… - arXiv preprint arXiv …, 2020 - arxiv.org
In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent
constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple:(i) …
constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple:(i) …