activity
20152021
most citedLearning Optimal Decision Trees from Large Datasets

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2021

Boolean Matrix Factorization with SAT and MaxSAT

Florent Avellaneda, Roger Villemaire

The Boolean matrix factorization problem consists in approximating a matrix by the Boolean product of two smaller Boolean matrices. To obtain optimal solutions when the matrices to…

cs.LG2020★ 1 cited

An Approach to Evaluating Learning Algorithms for Decision Trees

Tianqi Xiao, Omer Nguena Timo, Florent Avellaneda +2

Learning algorithms produce software models for realising critical classification tasks. Decision trees models are simpler than other models such as neural network and they are use…

cs.LG2019★ 4 cited

Learning Optimal Decision Trees from Large Datasets

Florent Avellaneda

Inferring a decision tree from a given dataset is one of the classic problems in machine learning. This problem consists of buildings, from a labelled dataset, a tree such that eac…

cs.SE2018

Fault Detection for Timed FSM with Timeouts by Constraint Solving

Omer Nguena Timo, Dimitri Prestat, Florent Avellaneda

Recently, an efficient constraint solving-based approach has been developed to detect logical faults in systems specified with classical finite state machines (FSMs). The approach…

cs.FL2015★ 1 cited

On the Complexity of Flanked Finite State Automata

Florent Avellaneda, Silvano Dal Zilio, Jean-Baptiste Raclet

We define a new subclass of nondeterministic finite automata for prefix-closed languages called Flanked Finite Automata (FFA). We show that this class enjoys good complexity proper…