KEEL-CTNC: APRENDIZAJE EVOLUTIVO DE SISTEMAS BASADOS EN REGLAS DIFUSAS, DATOS IMBALANCEADOS Y MINERIA DE PATRONES DE INTERES Y FRECUENTES. PREPROCESAMIENTO DE DATOSY ESCALABILIDAD DE LOS ALGORITMOS EN
TIN2008-06681-C06-01
•
Nombre agencia financiadora Ministerio de Ciencia e Innovación
Acrónimo agencia financiadora MICINN
Programa Programa Nacional de Investigación Fundamental
Subprograma Investigación fundamental no-orientada
Convocatoria Investigación fundamental no-orientada
Año convocatoria 2008
Unidad de gestión Subdirección General de Proyectos de Investigación
Centro beneficiario UNIVERSIDAD DE VALLADOLID (UVA)
Centro realización UNIVERSIDAD DE VALLADOLID (UVA)
Identificador persistente http://dx.doi.org/10.13039/501100004837
KEEL-CTNC: APRENDIZAJE EVOLUTIVO DE SISTEMAS BASADOS EN REGLAS DIFUSAS, DATOS IMBALANCEADOS Y MINERIA DE PATRONES DE INTERES Y FRECUENTES. PREPROCESAMIENTO DE DATOSY ESCALABILIDAD DE LOS ALGORITMOS EN
TIN2008-06681-C06-01
•
Nombre agencia financiadora Ministerio de Ciencia e Innovación
Acrónimo agencia financiadora MICINN
Programa Programa Nacional de Investigación Fundamental
Subprograma Investigación fundamental no-orientada
Convocatoria Investigación fundamental no-orientada
Año convocatoria 2008
Unidad de gestión Subdirección General de Proyectos de Investigación
Centro beneficiario UNIVERSIDAD DE GRANADA
Centro realización ESCUELA TECNICA SUPERIOR DE INGENIERIA INFORMÁTICA Y TELECOMUNICACIONES
Identificador persistente http://dx.doi.org/10.13039/501100004837
KEEL-CTNC: APRENDIZAJE EVOLUTIVO DE SISTEMAS BASADOS EN REGLAS DIFUSAS, DATOS IMBALANCEADOS Y MINERIA DE PATRONES DE INTERES Y FRECUENTES. PREPROCESAMIENTO DE DATOSY ESCALABILIDAD DE LOS ALGORITMOS EN
TIN2008-06681-C06-01
•
Nombre agencia financiadora Ministerio de Ciencia e Innovación
Acrónimo agencia financiadora MICINN
Programa Programa Nacional de Investigación Fundamental
Subprograma Investigación fundamental no-orientada
Convocatoria Investigación fundamental no-orientada
Año convocatoria 2008
Unidad de gestión Subdirección General de Proyectos de Investigación
Centro beneficiario NATIONAL UNIVERSITY OF IRELAND, GALWAY (NUI GALWAY) / UNIVERSIDAD NACIONAL DE IRLANDA
Centro realización SCHOOL OF COMPUTER SCIENCE
Identificador persistente http://dx.doi.org/10.13039/501100004837
Publicaciones
Resultados totales (Incluyendo duplicados): 2
Encontrada(s) 1 página(s)
Encontrada(s) 1 página(s)
Pre-aggregation functions: construction and an application
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Lucca, Giancarlo
- Sanz Delgado, José Antonio
- Pereira Dimuro, Graçaliz
- Callejas Bedregal, Benjamin
- Mesiar, Radko
- Kolesárová, Anna
- Bustince Sola, Humberto
In this work we introduce the notion of preaggregation
function. Such a function satisfies the same boundary
conditions as an aggregation function, but, instead of requiring
monotonicity, only monotonicity along some fixed direction (directional
monotonicity) is required. We present some examples
of such functions. We propose three different methods to build
pre-aggregation functions. We experimentally show that in fuzzy
rule-based classification systems, when we use one of these
methods, namely, the one based on the use of the Choquet
integral replacing the product by other aggregation functions,
if we consider the minimum or the Hamacher product t-norms
for such construction, we improve the results obtained when
applying the fuzzy reasoning methods obtained using two classical
averaging operators like the maximum and the Choquet integral., This work was supported in part by the Spanish Ministry of Science
and Technology under projects TIN2008-06681-C06-01, TIN2010-
15055, TIN2013-40765-P, TIN2011-29520.
function. Such a function satisfies the same boundary
conditions as an aggregation function, but, instead of requiring
monotonicity, only monotonicity along some fixed direction (directional
monotonicity) is required. We present some examples
of such functions. We propose three different methods to build
pre-aggregation functions. We experimentally show that in fuzzy
rule-based classification systems, when we use one of these
methods, namely, the one based on the use of the Choquet
integral replacing the product by other aggregation functions,
if we consider the minimum or the Hamacher product t-norms
for such construction, we improve the results obtained when
applying the fuzzy reasoning methods obtained using two classical
averaging operators like the maximum and the Choquet integral., This work was supported in part by the Spanish Ministry of Science
and Technology under projects TIN2008-06681-C06-01, TIN2010-
15055, TIN2013-40765-P, TIN2011-29520.
Proyecto: MICINN, MICINN, MINECO, MICINN//TIN2008-06681-C06-01, TIN2010-15055, TIN2013-40765-P, TIN2011-29520
A first study on the use of interval-valued fuzzy sets with genetic tuning for classification with imbalanced data sets
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Sanz Delgado, José Antonio
- Fernández, Alberto
- Bustince Sola, Humberto
- Herrera, Francisco
Classification with imbalanced data-sets is one of the recent
challenging problems in Data Mining. In this framework, the class dis-
tribution is not uniform and the separability between the classes is often
difficult. From the available techniques in the Machine Learning field,
we focus on the use of Fuzzy Rule Based Classification Systems, as they
provide an interpretable model for the end user by means of linguistic
variables.
The aim of this work is to increase the performance of fuzzy modeling
by adding a higher degree of knowledge by means of the use of Interval-
valued Fuzzy Sets. Furthermore, we will contextualize the Interval-valued
Fuzzy Sets with a post-processing genetic tuning of the amplitude of
their upper bounds in order to enhance the global behaviour of this
methodology., This work has been supported by the Spanish Ministry of Science and Technology
under projects TIN2008-06681-C06-01 and TIN2007-65981.
challenging problems in Data Mining. In this framework, the class dis-
tribution is not uniform and the separability between the classes is often
difficult. From the available techniques in the Machine Learning field,
we focus on the use of Fuzzy Rule Based Classification Systems, as they
provide an interpretable model for the end user by means of linguistic
variables.
The aim of this work is to increase the performance of fuzzy modeling
by adding a higher degree of knowledge by means of the use of Interval-
valued Fuzzy Sets. Furthermore, we will contextualize the Interval-valued
Fuzzy Sets with a post-processing genetic tuning of the amplitude of
their upper bounds in order to enhance the global behaviour of this
methodology., This work has been supported by the Spanish Ministry of Science and Technology
under projects TIN2008-06681-C06-01 and TIN2007-65981.