Yves Lechevallier

Publications (7)0 Total impact

• Article: Hierarchical clustering of subpopulations with a dissimilarity based on the likelihood ratio statistic: application to clustering massive data sets.

  Antonio Ciampi, Yves Lechevallier, Manuel Castejón Limas, Ana González-Marcos
  Pattern Anal. Appl. 01/2008; 11:199-220.
• Source

  Available from: medicine.mcgill.ca

  Chapter: Statistical Models and Artificial Neural Networks: Supervised Classification and Prediction Via Soft Trees

  Antonio Ciampi, Yves Lechevallier
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  ABSTRACT: It is well known that any statistical model for supervised or unsupervised classification can be realized as a neural network. This discussion is devoted to supervised classification and therefore the essential framework is the family of feedforward nets. Ciampi and Lechevallier have studied two- and three-hidden-layer feedforward neural nets that are equivalent to trees, characterized by neurons with “hard” thresholds. Softening the thresholds has led to more general models. Also, neural nets that realize additive models have been studied, as well as networks of networks that represent a “mixed” classifier (predictor) consisting of a tree component and an additive component. Various “dependent” variables have been studied, including the case of censored survival times. A new development has recently been proposed: the soft tree. A soft tree can be represented as a particular type of hierarchy of experts. This representation can be shown to be equivalent to that of Ciampi and Lechevallier. However, it leads to an appealing interpretation, to other possible generalizations and to a new approach to training. Soft trees for classification and prediction of a continuous variable will be presented. Comparisons between conventional trees (trees with hard thresholds) and soft trees will be discussed and it will be shown that the soft trees achieve better predictions than the hard tree.
  12/2006: pages 239-261;
• Source

  Available from: medicine.mcgill.ca

  Chapter: Multilevel Clustering for Large Databases

  Yves Lechevallier, Antonio Ciampi
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  ABSTRACT: Standard clustering methods do not handle truly large data sets and fail to take into account multilevel data structures. This work outlines an approach to clustering that integrates the Kohonen Self-Organizing Map (SOM) with other clustering methods. Moreover, in order to take into account multilevel structures, a statistical model is proposed, in which a mixture of distributions may have mixing coefficients depending on higher-level variables. Thus, in a first step, the SOM provides a substantial data reduction, whereby a variety of ascending and divisive clustering algorithms becomes accessible. As a second step, statistical modeling provides both a direct means to treat multilevel structures and a framework for model-based clustering. The interplay of these two steps is illustrated on an example of nutritional data from a multicenter study on nutrition and cancer, known as EPIC.
  12/2006: pages 263-274;
• Conference Proceeding: Clustering Large, Multi-level Data Sets: An Apporach Based on Kohonen Self Organizing Maps.

  Antonio Ciampi, Yves Lechevallier
  Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000, Lyon, France, September 13-16, 2000, Proceedings; 01/2000
• Chapter: Clustering Large, Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps

  Antonio Ciampi, Yves Lechevallier
  [show abstract] [hide abstract]
  ABSTRACT: Standard clustering methods do not handle truly large data sets and fail to take into account multi-level data structures. This work outlines an approach to clustering that integrates the Kohonen Self Organizing Map (SOM) with other clustering methods. Moreover, in order to take into account multi-level structures, a statistical model is proposed, in which a mixture of distributions may have mixing coefficients depending on higher-level variables. Thus, in a first step, the SOM provides a substantial data reduction, whereby a variety of ascending and divisive clustering algorithms become accessible. As a second step, statistical modelling provides both a direct means to treat multi-level structures and a framework for model-based clustering. The interplay of these two steps is illustrated on an example of nutritional data from a multi-center study on nutrition and cancer, known as EPIC.
  12/1999: pages 161-177;
• Article: Statistical models as building blocks of neural networks

  Antonio Ciampi, Yves Lechevallier
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  ABSTRACT: The interplay of neural networks and statistical modeling is discussed in the context of the classification problem. It is shown that, on the one hand, the statistical modeling approach provides a systematic way of obtaining good initializations in the neural network context, while, on the other, neural networks offer a powerful expansion to classical model families. A novel integrated approach emerges: statistical models are used as building blocks of neural architectures. The result is an improvement in both flexibility (contribution of neural nets) and interpretability (contribution of statistical modeling).
  Communications in Statistics - Theory and Methods. 01/1997; 26(4):991-1009.
• Conference Proceeding: Designing Neural Networks from Statistical Models: A New Approach to Data Exploration.

  Antonio Ciampi, Yves Lechevallier
  01/1995