Learning Optimal Decision Trees using Constraint Programming
Decision trees are among the most popular classification models in machine learning. Using greedy algorithms to learn them can pose several disadvantages. For these reasons, there has been a recent interest in exact and flexible algorithms for learning decision trees. In our paper, we introduce a new approach to learn decision trees using constraint programming.
Hélène is a 5th year PhD student. The topic of her thesis is the extensional constraint in constraint programming. She is interested in the use of constraint programming to solve machine learning problems. She also likes cows and cats and is always interested to learn new things.