Actualidad

Seminario: Practical Implications of Classification Calibration

El 13 de enero de 2014 a las 12:00 h. en el Salón de Grados de la Escuela Politécnica Superior de la Universidad Autónoma de Madrid tiene lugar el seminario «Practical Implications of Classification Calibration», impartido por Irene Rodríguez Luján, del Biocircuits Institute, University of California S. Diego.

Resumen/Abstract

The importance of classification calibration falls on its relationship with the Bayes consistency. Most research in the literature has analyzed classification calibration under a mathematical framework that implicitly assumes that the decision functions can be separately defined for each point. Therefore, this framework generally overlooks the practical consequences of using a specific classification model based on these loss functions.

Therefore, the goal of this seminar is to give practical point of view of the classification calibration concept by trying to address the following questions: Is it possible to have a continuous family of loss functions in such a way that we can easily control their classification calibration properties? Are the classification calibration requirements feasible when such set of functions is used with a parametric classifier? In other words, does the classifier inherit the classification calibration properties of the loss function?

We propose a continuous family of loss functions defined by a control parameter that allows us to shift the loss function from classification uncalibrated to calibrated. We characterize the decision functions that make a loss function classification calibrated to determine whether these decision functions are achievable in parametric classifiers. As an example, we embed this continuous family of loss functions in a multiclass Support Vector Machine (SVM) to analyze SVM's solutions as a function of the control parameter, obtaining as byproduct a new classification model when the control parameter tends to infinity. Our experiments on multiclass problems show similar classification accuracy for classification calibrated and uncalibrated loss functions, and they point to the classifier with the control parameter tending to infinity as a promising model in terms of classification accuracy and training time.

Dr. Irene Rodríguez Luján. Se graduó en Ingeniería Informática y Matemáticas en la Universidad Universidad Autónoma de Madrid, doctorándose en Ingeniería Informática por la misma universidad en 2012. Entre 2005 y 2011 colaboró con el Instituto de Ingeniería del Conocimiento,y entre 2011 y 2013 trabajó como investigadora en el Grupo de Biometría, Bioseñales y Seguridad de la Universidad Politécnica de Madrid. Desde marzo de 2013 es investigadora postdoctoral en el BioCircuits Institute de la Universidad de California San Diego (UCSD). Sus intereses se centran en el ámbito del reconocimiento de patrones y sus aplicaciones prácticas.