153 research outputs found
Empirical evaluation of optimized stacking configurations
Proceeding of: 16th IEEE International Conference on Tools with Artificial Intelligence, 15-17 Nov. 2004, Boca Ratón, FloridaStacking is one of the most used techniques for combining classifiers and improves prediction accuracy. Early research in stacking showed that selecting the right classifiers, their parameters and the metaclassifiers was the main bottleneck for its use. Most of the research on this topic selects by hand the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good stacking configurations. Since this can lead to overfitting, one of the goals of This work is to evaluate empirically the overall efficiency of the approach. A second goal is to compare our approach with current best stacking building techniques. The results show that our approach finds stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to set up manually the structure of the stacking system.Publicad
Various Approaches of Support vector Machines and combined Classifiers in Face Recognition
In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition
Un nuevo conjunto de datos para la detección de roya en cultivos de café Colombianos basado en clasificadores
Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.La producción de café es la principal actividad agrícola en Colombia. Más de 350.000 familias colombianas dependen de la cosecha de café. En este sentido, la roya fue reportada por primera vez en el país en 1983, y desde entonces estas familias han tenido que enfrentar graves consecuencias. Recientemente, diversos enfoques basados en aprendizaje automático han construido un conjunto de datos para el monitoreo de la incidencia de la roya del café, teniendo en cuenta las condiciones climáticas y las propiedades físicas de los cultivos. Estas investigaciones motivaron la creación de un conjunto de datos para la detección de la roya en cultivos Colombianos a través del proceso de minería de datos CRISP-DM. En este trabajo se definió un conjunto de datos con el objetivo de generar clasificadores precisos; una vez construido el conjunto de datos, fue probado mediante tres clasificadores: Maquinas de vector de regresión, Redes neuronales con propagación hacia atrás y Árboles de regresión
GA-stacking: Evolutionary stacked generalization
Stacking is a widely used technique for combining classifiers and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers, their parameters and the meta-classifiers was a critical issue. Most of the research on this topic hand picks the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good Stacking configurations. Since this can lead to overfitting, one of the goals of this paper is to empirically evaluate the overall efficiency of the approach. A second goal is to compare our approach with the current best Stacking building techniques. The results show that our approach finds Stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to manually set up the structure of the Stacking system.This work has been partially supported by the Spanish MCyT under projects TRA2007-67374-C02-02
and TIN-2005-08818-C04. Also, it has been supported under MEC grant by TIN2005-08945-C06-05.
We thank anonymous reviewers for their helpful comments.Publicad
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
Recently, deep learning has emerged as a promising tool for statistical
downscaling, the set of methods for generating high-resolution climate fields
from coarse low-resolution variables. Nevertheless, their ability to generalize
to climate change conditions remains questionable, mainly due to the
stationarity assumption. We propose deep ensembles as a simple method to
improve the uncertainty quantification of statistical downscaling models. By
better capturing uncertainty, statistical downscaling models allow for superior
planning against extreme weather events, a source of various negative social
and economic impacts. Since no observational future data exists, we rely on a
pseudo reality experiment to assess the suitability of deep ensembles for
quantifying the uncertainty of climate change projections. Deep ensembles allow
for a better risk assessment, highly demanded by sectoral applications to
tackle climate change.Comment: Accepted at the ICLR 2023 Tackling Climate Change with Machine
Learning Worksho
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