Persona:
Amigo Cabrera, Enrique

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0003-1482-824X
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Amigo Cabrera
Nombre de pila
Enrique
Nombre

Filtros

2009 - 200912010 - 20111
Restablecer filtros

Ajustes

Autor: Artiles, Javier × search.filters.applied.f.itemDepartamento: Lenguajes y Sistemas Informáticos ×

Resultados de la búsqueda

Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Combining evaluation metrics via the unanimous improvement ratio and its application in weps clustering task
    (Association for the Advancement of Artificial Intelligence, 2011-12-01) Artiles, Javier; Verdejo, Felisa; Amigo Cabrera, Enrique; Gonzalo Arroyo, Julio Antonio
    Many Artificial Intelligence tasks cannot be evaluated with a single quality criterion and some sort of weighted combination is needed to provide system rankings. A problem of weighted combination measures is that slight changes in the relative weights may produce substantial changes in the system rankings. This paper introduces the Unanimous Improvement Ratio (UIR), a measure that complements standard metric combination criteria (such as van Rijsbergen's F-measure) and indicates how robust the measured differences are to changes in the relative weights of the individual metrics. UIR is meant to elucidate whether a perceived difference between two systems is an artifact of how individual metrics are weighted. Besides discussing the theoretical foundations of UIR, this paper presents empirical results that confirm the validity and usefulness of the metric for the Text Clustering problem, where there is a tradeoff between precision and recall based metrics and results are particularly sensitive to the weighting scheme used to combine them. Remarkably, our experiments show that UIR can be used as a predictor of how well differences between systems measured on a given test bed will also hold in a different test bed.
  • Miniatura
    Publicación
    A comparison of extrinsic clustering evaluation metrics based on formal constraints
    (Springer, 2009-05-11) Artiles, Javier; Verdejo, Felisa; Amigo Cabrera, Enrique; Gonzalo Arroyo, Julio Antonio
    There is a wide set of evaluation metrics available to compare the quality of text clustering algorithms. In this article, we define a few intuitive formal constraints on such metrics which shed light on which aspects of the quality of a clustering are captured by different metric families. These formal constraints are validated in an experiment involving human assessments, and compared with other constraints proposed in the literature. Our analysis of a wide range of metrics shows that only BCubed satisfies all formal constraints. We also extend the analysis to the problem of overlapping clustering, where items can simultaneously belong to more than one cluster. As Bcubed cannot be directly applied to this task, we propose a modified version of Bcubed that avoids the problems found with other metrics.