List of Accepted Papers for Track ASAI 2009

Los sistemas de recomendación se utilizan para realizar recomendaciones de ítems potencialmente interesantes para un usuario en variados dominios. Existe un gran número de dominios que sugieren la necesidad de proveer técnicas de personalización para grupos de usuarios y no sólo focalizarse en usuarios individuales. En este trabajo se presentan dos aplicaciones que implementan técnicas de generación de recomendaciones grupales a partir del cual se realizaron dos instanciaciones en dominios diferentes: recomendación de música y recomendación de películas. A partir de estas aplicaciones se analizaron y compararon las distintas técnicas evaluando su funcionamiento en distintos dominios de aplicación. Feature selection on wide multiclass problems using OVA-RFE. Pablo M. Granitto and Andrés Burgos. Abstract: Feature selection is a pre–processing technique commonly used with high– dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. For multiclass classification problems, recent works suggested that decomposing the multiclass problem in a set of binary ones, and doing the feature selection on the binary problems could be a sound strategy. In this work we combined the well–known Recursive Feature Elimination (RFE) algorithm with the simple One–Vs–All (OVA) technique for multiclass problems, to produce the new OVA–RFE selection method. We evaluated OVA–RFE using wide datasets from genomic and mass–spectrometry analysis, and several classifiers. In particular, we compared the new method with the traditional RFE (applied to a direct multiclass classifier) in terms of accuracy and stability. Our results show that OVA–RFE is no better than the traditional method, which is in opposition to previous results on similar methods. The opposite results are related to a different interpretation of the real number of variables in use by both methods. Feature selection is a pre–processing technique commonly used with high– dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase the performance and interpretability of models. For multiclass classification problems, recent works suggested that decomposing the multiclass problem in a set of binary ones, and doing the feature selection on the binary problems could be a sound strategy. In this work we combined the well–known Recursive Feature Elimination (RFE) algorithm with the simple One–Vs–All (OVA) technique for multiclass problems, to produce the new OVA–RFE selection method. We evaluated OVA–RFE using wide datasets from genomic and mass–spectrometry analysis, and several classifiers. In particular, we compared the new method with the traditional RFE (applied to a direct multiclass classifier) in terms of accuracy and stability. Our results show that OVA–RFE is no better than the traditional method, which is in opposition to previous results on similar methods. The opposite results are related to a different interpretation of the real number of variables in use by both methods. Gaining knowledge of data structure using stability concepts. Ariel Baya and Pablo