M. Boullé

Publications131
h-index17
Citations1,410
Highly Influential Citations105
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  • Publications
  • Influence
MODL: A Bayes optimal discretization method for continuous attributes
  • M. Boullé
  • Mathematics, Computer Science
  • Machine Learning
  • 1 October 2006
TLDR
We propose a new discretization method MODL1, founded on a Bayesian approach. Expand
  • 171
  • 16
  • PDF
Khiops: A Statistical Discretization Method of Continuous Attributes
  • M. Boullé
  • Computer Science
  • Machine Learning
  • 1 April 2004
TLDR
In supervised machine learning, some algorithms are restricted to discrete data and have to discretize continuous attributes, i.e. to slice their domain into a finite number of intervals. Expand
  • 114
  • 12
  • PDF
Compression-Based Averaging of Selective Naive Bayes Classifiers
  • M. Boullé
  • Computer Science, Mathematics
  • J. Mach. Learn. Res.
  • 1 December 2007
TLDR
We introduce a Bayesian regularization technique to select the most probable subset of variables compliant with the naive Bayes assumption and introduce a new weighting scheme based on the ability of the models to conditionally compress the class labels. Expand
  • 107
  • 10
  • PDF
Comparing State-of-the-Art Collaborative Filtering Systems
TLDR
We present here many different options of the three general approaches for collaborative filtering: 1. collaborative filtering, 2. content-based filtering, 3. hybrid filtering. Expand
  • 172
  • 9
  • PDF
Design and analysis of the KDD cup 2009: fast scoring on a large orange customer database
TLDR
We organized the KDD cup 2009 around a marketing problem with the goal of identifying data mining techniques capable of rapidly building predictive models and scoring new entries on a large database. Expand
  • 61
  • 7
  • PDF
A user parameter-free approach for mining robust sequential classification rules
TLDR
We propose a new approach and framework for mining sequential rule patterns for classification purpose and propose a Bayesian criterion for evaluating the interest of sequential patterns. Expand
  • 20
  • 7
  • PDF
Analysis of the AutoML Challenge Series 2015-2018
TLDR
The ChaLearn AutoML Challenge (The authors are in alphabetical order of last name, except the first author who did most of the writing and the second author who produced most the numerical analyses and plots.) (NIPS 2015 – ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty. Expand
  • 47
  • 4
  • PDF
Discovering patterns in time-varying graphs: a triclustering approach
TLDR
This paper introduces a novel technique to track structures in time varying graphs. Expand
  • 20
  • 4
  • PDF
A Triclustering Approach for Time Evolving Graphs
TLDR
This paper introduces a novel technique to track structures in time evolving graphs. Expand
  • 19
  • 3
  • PDF
A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data
TLDR
We introduce a propositionalization approach dedicated to a robust Bayesian classifier that can learn complex aggregates. Expand
  • 10
  • 3
  • PDF