Session TC5 - Classification / Clustering
Day Tuesday, May 05, 2009 Room Ordre des CGA President Sylvain Perron
Presentations
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03h30 PM- 03h55 PM |
Parallel Hyperplanes Separation Method for the Two-Groups Discrimination Problem |
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Anthony Guillou, GERAD, HEC Montréal, Montréal, Québec, Canada Pierre Hansen, HEC Montréal, GERAD et Méthodes quantitatives de gestion, 3000 Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 Sylvain Perron, GERAD, HEC Montréal, Méthodes quantitatives de gestion, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 We consider the problem of separating two non linearly separable sets of points A and B in the euclidean space, with two parallel hyperplanes that are respectively the boundaries of half-spaces containing A and B. Both heuristic and exact method are proposed to minimise the euclidean distance between such two hyperplanes. |
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03h55 PM- 04h20 PM |
Optimal Clusterwise Multiple Linear Regression |
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Réal Carbonneau, GERAD et HEC Montréal, Méthodes quantitatives de gestion Gilles Caporossi, GERAD, HEC Montréal, Méthodes quantitatives de gestion, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 Pierre Hansen, HEC Montréal, GERAD et Méthodes quantitatives de gestion, 3000 Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 Clusterwise regression has been used as a data mining tool, however, there is no published research on identifying optimal solutions. In this research, both a quadratic programming formulation and a branch and bound algorithm are compared for identifying Optimal Clusterwise Multiple Linear Regression (OCMLR) solutions. The processing time increases with the number of clusters, observations, dimensions and amount of noise. |
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04h20 PM- 04h45 PM |
A Column Generation Algorithm for 2 Groups Discrimination |
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Gilles Caporossi, GERAD, HEC Montréal, Méthodes quantitatives de gestion, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 Sylvain Perron, GERAD, HEC Montréal, Méthodes quantitatives de gestion, 3000, chemin de la Côte-Sainte-Catherine, Montréal, Québec, Canada, H3T 2A7 A method of discrimination among two groups of data is proposed. This method involves a combination of linear classifiers, each of which aims at minimizing the number of (weighted) misclassified observations. The combination of these classifiers is achieved by weighted vote. The learning algorithm uses column generation and could be viewed as a mathematical programming approach for boosting. |