J. S. Sánchez

发表

Combined Effects of Class Imbalance and Class Overlap on Instance-Based Classification

José Martínez Sotoca, Roberto Alejo, José Salvador Sánchez, 2006, IDEAL.

Analysis of new techniques to obtain quality training sets

Roberto Alejo, José Salvador Sánchez, Ricardo Barandela, 2003, Pattern Recognit. Lett..

On the use of neighbourhood-based non-parametric classifiers

Filiberto Pla, José Salvador Sánchez, Francesc J. Ferri, 1997, Pattern Recognit. Lett..

The Imbalanced Training Sample Problem: Under or over Sampling?

Rosa Maria Valdovinos, José Salvador Sánchez, Ricardo Barandela, 2004, SSPR/SPR.

Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (suema'2007) Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (suema'2007) in Conjunction with the 3rd Iberian Conference on Pattern Recognition and Image Analysis Clustering Ensembles for

J. S. Sánchez, J. S. Sánchez, 2007 .

On the suitability of resampling techniques for the class imbalance problem in credit scoring

José Salvador Sánchez, A. I. Marqués, Vicente García, 2013, J. Oper. Res. Soc..

Making Accurate Credit Risk Predictions with Cost-Sensitive MLP Neural Networks

José Salvador Sánchez, Vicente García, A. I. Marqués, 2013 .

Improving the Performance of the RBF Neural Networks Trained with Imbalanced Samples

José Martínez Sotoca, Roberto Alejo, José Salvador Sánchez, 2007, IWANN.

Mapping microarray gene expression data into dissimilarity spaces for tumor classification

José Salvador Sánchez, Vicente García, V. García, 2015, Inf. Sci..

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

A. I. Marqués, V. García, J. S. Sánchez, 2015, Journal of Intelligent Information Systems.

Two-level classifier ensembles for credit risk assessment

José Salvador Sánchez, Vicente García, A. I. Marqués, 2012, Expert Syst. Appl..

Exploring the behaviour of base classifiers in credit scoring ensembles

A. I. Marqués, J. S. Sánchez, V. García, 2012, Expert Syst. Appl..

A bias correction function for classification performance assessment in two-class imbalanced problems

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2014, Knowl. Based Syst..

One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices

José Salvador Sánchez, Mónica Millán-Giraldo, Vicente García, 2012, SSPR/SPR.

Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2009, IbPRIA.

When Overlapping Unexpectedly Alters the Class Imbalance Effects

José Martínez Sotoca, Roberto Alejo, José Salvador Sánchez, 2007, IbPRIA.

Learning from Imbalanced Sets through Resampling and Weighting

José Salvador Sánchez, Ricardo Barandela, Francesc J. Ferri, 2003, IbPRIA.

On the k-NN performance in a challenging scenario of imbalance and overlapping

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2008, Pattern Analysis and Applications.

Imbalanced Training Set Reduction and Feature Selection Through Genetic Optimization

José Salvador Sánchez, Ricardo Barandela, Francesc J. Ferri, 2005, CCIA.

New Applications of Ensembles of Classifiers

Rosa Maria Valdovinos, José Salvador Sánchez, Ricardo Barandela, 2003, Pattern Analysis & Applications.

Restricted Decontamination for the Imbalanced Training Sample Problem

José Salvador Sánchez, Ricardo Barandela, Francesc J. Ferri, 2003, CIARP.

On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2012, Knowl. Based Syst..

An analysis of how training data complexity affects the nearest neighbor classifiers

José Martínez Sotoca, José Salvador Sánchez, Ramón Alberto Mollineda, 2007, Pattern Analysis and Applications.

Exploring Early Classification Strategies of Streaming Data with Delayed Attributes

José Salvador Sánchez, V. Javier Traver, Mónica Millán-Giraldo, 2009, ICONIP.

Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis

V. García, J. S. Sánchez, L. Palomero, 2022, Applied Sciences.

An Empirical Study of the Behavior of Classifiers on Imbalanced and Overlapped Data Sets

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2007, CIARP.

Improving Risk Predictions by Preprocessing Imbalanced Credit Data

José Salvador Sánchez, A. I. Marqués, Vicente García, 2012, ICONIP.

Surrounding neighborhood-based SMOTE for learning from imbalanced data sets

R. A. Mollineda, V. García, R. Martín-Félez, 2012, Progress in Artificial Intelligence.

Exploring the Performance of Resampling Strategies for the Class Imbalance Problem

José Salvador Sánchez, Ramón Alberto Mollineda, Vicente García, 2010, IEA/AIE.

Prototype selection for the nearest neighbour rule through proximity graphs

Filiberto Pla, José Salvador Sánchez, Francesc J. Ferri, 1997, Pattern Recognit. Lett..

Eliminating redundancy and irrelevance using a new MLP-based feature selection method

Eduardo Gasca, José Salvador Sánchez, R. Alonso, 2006, Pattern Recognit..

A New Under-Sampling Method to Face Class Overlap and Imbalance

José Raymundo Marcial-Romero, José Salvador Sánchez, R. M. Valdovinos, 2020, Applied Sciences.

Human Recognition Based on Gait Poses

José Salvador Sánchez, Raúl Martín-Félez, Ramón Alberto Mollineda, 2011, IbPRIA.

Hybrid Associative Memories for Imbalanced Data Classification: An Experimental Study

R. RosaM.Valdovinos, José Salvador Sánchez, Oscar Camacho Nieto, 2013, MCPR.

Enhanced Neighbourhood Specifications for Pattern Classification

José Salvador Sánchez, A. I. Marqués, J. S. Sánchez, 2003 .

Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data

Rogelio Florencia Juárez, A. I. Marqués, Vicente García, 2020, Expert Syst. Appl..

DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem

José Salvador Sánchez, A. Guzmán-Ponce, R. M. Valdovinos, 2020, Expert Syst. Appl..

High training set size reduction by space partitioning and prototype abstraction

José Salvador Sánchez, J. S. Sánchez, J. S. Sánchez, 2004, Pattern Recognit..

Instance Selection Methods and Resampling Techniques for Dissimilarity Representation with Imbalanced Data Sets

José Salvador Sánchez, Vicente García, M. Millán-Giraldo, 2013 .

Prototype Selection in Imbalanced Data for Dissimilarity Representation - A Preliminary Study

José Salvador Sánchez, Mónica Millán-Giraldo, Vicente García, 2012, ICPRAM.

Nearest Neighbour Editing and Condensing Tools–Synergy Exploitation

Belur V. Dasarathy, José Salvador Sánchez, S. Townsend, 2000, Pattern Analysis & Applications.

Combining Multiple Classifiers with Dynamic Weighted Voting

Rosa Maria Valdovinos, José Salvador Sánchez, R. M. Valdovinos, 2009, HAIS.

Data Characterization for Effective Prototype Selection

José Martínez Sotoca, José Salvador Sánchez, Ramón Alberto Mollineda, 2005, IbPRIA.

Strategies for learning in class imbalance problems

José Salvador Sánchez, Ricardo Barandela, Vicente García, 2003, Pattern Recognit..

José Salvador Sánchez, Humberto de Jesús Ochoa Domínguez, Vicente García, 2015, IbPRIA.

On the use of data filtering techniques for credit risk prediction with instance-based models

José Salvador Sánchez, Vicente García, A. I. Marqués, 2012, Expert Syst. Appl..

Special Issue on “ Pattern Recognition for Autonomous Manipulation in Robotic Systems”

José Salvador Sánchez, Pedro J. Sanz, Raul Marin, 2005 .

Pattern Recognition and Image Analysis: 9th Iberian Conference, IbPRIA 2019, Madrid, Spain, July 1–4, 2019, Proceedings, Part II

Bernardete Ribeiro, José Salvador Sánchez, Julian Fierrez, 2019, IbPRIA.

On-line learning from streaming data with delayed attributes: a comparison of classifiers and strategies

José Salvador Sánchez, V. Javier Traver, Mónica Millán-Giraldo, 2011, Neural Computing and Applications.

A comparative study of simple online learning strategies for streaming data

José Salvador Sánchez, M. Millan-Giraldo, M. Millán-Giraldo, 2008 .

Strategies for learning in class imbalance problems

R. Barandelaa, E. Rangela, R. Barandela, 2003, Pattern Recognit..

A regression model based on the nearest centroid neighborhood

A. I. Marqués, V. García, R. Martínez-Peláez, 2018, Pattern Analysis and Applications.

Nearest Neighbour Classifiers for Streaming Data with Delayed Labelling

Ludmila I. Kuncheva, J. Salvador Sánchez, L. Kuncheva, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Associative learning on imbalanced environments: An empirical study

R. RosaM.Valdovinos, José Salvador Sánchez, Vicente García, 2016, Expert Syst. Appl..

Financial distress prediction using the hybrid associative memory with translation

José Salvador Sánchez, Vicente García, A. I. Marqués, 2016, Appl. Soft Comput..

A literature review on the application of evolutionary computing to credit scoring

José Salvador Sánchez, Vicente García, A. I. Marqués, 2013, J. Oper. Res. Soc..

Decision boundary preserving prototype selection for nearest neighbor classification

José Salvador Sánchez, Ricardo Barandela, Francesc J. Ferri, 2005, Int. J. Pattern Recognit. Artif. Intell..

A meta-learning framework for pattern classification by means of data complexity measures

José Martínez Sotoca, José Salvador Sánchez, Ramón Alberto Mollineda, 2006, Inteligencia Artif..

Model Selection for Financial Distress Prediction by Aggregating TOPSIS and PROMETHEE Rankings

José Salvador Sánchez, A. I. Marqués, Vicente García, 2016, HAIS.

Assessment of Financial Risk Prediction Models with Multi-criteria Decision Making Methods

José Salvador Sánchez, Vicente García, A. I. Marqués, 2012, ICONIP.

On-Line Classification of Data Streams with Missing Values Based on Reinforcement Learning

José Salvador Sánchez, V. Javier Traver, Mónica Millán-Giraldo, 2011, IbPRIA.

Eliminating redundancy and irrelevance using a new MLP-based feature selection method

J. S. Sánchez, R. Alonso, Eduardo Gasca, 2006, Pattern Recognit..

Robert P. W. Duin, José Salvador Sánchez, Mónica Millán-Giraldo, 2010, 2010 2nd International Workshop on Cognitive Information Processing.

An LVQ-based adaptive algorithm for learning from very small codebooks

José Salvador Sánchez, A. I. Marqués, J. S. Sánchez, 2006, Neurocomputing.