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1994, Nueva biblioteca de erudición y crítica, ISBN 9788470396854, Volume 7, xviii, 1047
Book
Knowledge-Based Systems, ISSN 0950-7051, 07/2018, Volume 152, p. 83
Twin Support Vector Regression is an effective machine learning strategy, which splits the predictive task into two small problems, gaining in both efficiency... 
Support vector machines | Uncertainty | Robustness (mathematics) | Efficiency | Machine learning | Regression | Performance prediction | Regression analysis | Artificial intelligence | Optimization
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 09/2017, Volume 132, p. 119
In this work, a novel feature selection method for twin Support Vector Machine (SVM) is presented. The main idea is to combine two regularizers, namely the... 
Support vector machines | Datasets | Classifiers | Automatic classification | Classification | Hyperplanes | Performance enhancement | Optimization
Journal Article
European Journal of Operational Research, ISSN 0377-2217, 12/2019
Journal Article
European Journal of Operational Research, ISSN 0377-2217, 09/2017, Volume 261, Issue 2, pp. 656 - 665
In this work we propose two formulations based on Support Vector Machines for simultaneous classification and feature selection that explicitly incorporate... 
Feature selection | Support Vector Machines | Mixed-integer programming | Analytics | Credit scoring | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CLASSIFICATION | Acquisitions and mergers | Analysis | Integer programming | Usage | Machine learning | Innovations
Journal Article
Information Sciences, ISSN 0020-0255, 12/2014, Volume 286, pp. 228 - 246
Feature selection and classification of imbalanced data sets are two of the most interesting machine learning challenges, attracting a growing attention from... 
Dimensionality reduction | Feature selection | Support Vector Machine | Data mining | Imbalanced data set | SURVIVAL | GENE SELECTION | CARCINOMAS | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 12/2019, Volume 137, pp. 59 - 73
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 08/2019, Volume 177, pp. 127 - 135
In this paper, we propose novel second-order cone programming formulations for binary classification, by extending the Minimax Probability Machine (MPM)... 
Support vector machines | Regularization | Second-order cone programming | Minimax probability machine | Formulations | Minimax technique | Classification | Performance prediction | Cone classifiers | Probabilistic methods | Ill-posed problems (mathematics)
Journal Article
Intelligent Data Analysis, ISSN 1088-467X, 09/2015, Volume 19, Issue 1, pp. S135 - S147
An empirical framework for customer churn prediction modeling is presented in this work. This task represents a very interesting business analytics challenge,... 
Support vector machines | support vector data description | class imbalance problem | data mining | feature selection | FEATURE-SELECTION | MACHINES | MODELS | IMBALANCED DATA | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ACCURACY
Journal Article
Information Sciences, ISSN 0020-0255, 2009, Volume 179, Issue 13, pp. 2208 - 2217
We introduce a novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions. Our method is based on a , using the number... 
Wrapper methods | Feature selection | Support Vector Machines | Classification | Mathematical programming | COMPUTER SCIENCE, INFORMATION SYSTEMS | Analysis | Methods | Algorithms
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 04/2020, Volume 143, p. 112988
In this study, an expert system is presented for analyzing the mental workload of interacting with a mobile phone while facing common daily tasks.... 
Support vector machines | Heterogeneity control | Feature selection | Group penalty functions | Mental workload
Journal Article
Applied Intelligence, ISSN 0924-669X, 12/2019, Volume 49, Issue 12, pp. 4223 - 4236
In this work, a novel method called epsilon-nonparallel support vector regression (ε-NPSVR) is proposed. The reasoning behind the nonparallel support vector... 
Twin support vector regression | Artificial Intelligence | Computer Science | Nonparallel support vector machines | Support vector regression | Mechanical Engineering | Manufacturing, Machines, Tools, Processes
Journal Article
Knowledge-Based Systems, ISSN 0950-7051, 05/2018, Volume 148, p. 41
In this work, the classical soft-margin Support Vector Machine (SVM) formulation is redefined with the inclusion of an Ordered Weighted Averaging (OWA)... 
Support vector machines | Operators (mathematics) | Slack variables | Training | Fuzzy logic | Algorithms | Kernel functions | Performance prediction | Pattern recognition | Data mining | Artificial intelligence
Journal Article
Decision Support Systems, ISSN 0167-9236, 12/2017, Volume 104, pp. 113 - 121
In this paper, we propose a profit-driven approach for classifier construction and simultaneous variable selection based on linear Support Vector Machines. The... 
Profit measure | Group penalty | Support Vector Machines | Credit scoring | Analytics | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COMPUTER SCIENCE, INFORMATION SYSTEMS | NETWORKS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Credit ratings
Journal Article
Applied Soft Computing Journal, ISSN 1568-4946, 06/2018, Volume 67, pp. 94 - 105
In this work, we propose a novel feature selection approach designed to deal with two major issues in machine learning, namely class-imbalance and high... 
Support Vector Data Description | Feature selection | Cost-sensitive learning | Imbalanced data classification | Embedded approaches | Machine learning
Journal Article
Applied Intelligence, ISSN 0924-669X, 06/2017, Volume 46, Issue 4, pp. 983 - 992
Kernel methods are very important in pattern analysis due to their ability to capture nonlinear relationships in datasets. The best known kernel-based... 
Support vector machines | Kernel methods | Multiclass classification | Second-order cone programming | FEATURE-SELECTION | TOOLBOX | FORMULATIONS | CLASSIFICATION | OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Formulations | Tasks | Data sets | Programming | Nonlinearity | Robustness | Optimization
Journal Article
Applied Soft Computing, ISSN 1568-4946, 10/2015, Volume 35, pp. 740 - 748
Churn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective... 
Support vector machines | Customer retention | Feature selection | Churn prediction | Data mining | Maximum profit | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | WRAPPER | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Customer loyalty | Customer relations
Journal Article
by Pons-Estel, Bernardo A and Bonfa, Eloisa and Soriano, Enrique R and Cardiel, Mario H and Izcovich, Ariel and Popoff, Federico and Criniti, Juan M and Vásquez, Gloria and Massardo, Loreto and Duarte, Margarita and Barile-Fabris, Leonor A and García, Mercedes A and Amigo, Mary-Carmen and Espada, Graciela and Catoggio, Luis J and Sato, Emilia Inoue and Levy, Roger A and Acevedo Vásquez, Eduardo M and Chacón-Díaz, Rosa and Galarza-Maldonado, Claudio M and Iglesias Gamarra, Antonio J and Molina, José Fernando and Neira, Oscar and Silva, Clóvis A and Vargas Peña, Andrea and Gómez-Puerta, José A and Scolnik, Marina and Pons-Estel, Guillermo J and Ugolini-Lopes, Michelle R and Savio, Verónica and Drenkard, Cristina and Alvarellos, Alejandro J and Ugarte-Gil, Manuel F and Babini, Alejandra and Cavalcanti, André and Cardoso Linhares, Fernanda Athayde and Haye Salinas, Maria Jezabel and Fuentes-Silva, Yurilis J and Montandon de Oliveira e Silva, Ana Carolina and Eraso Garnica, Ruth M and Herrera Uribe, Sebastián and Gómez-Martín, Diana and Robaina Sevrini, Ricardo and Quintana, Rosana M and Gordon, Sergio and Fragoso-Loyo, Hilda and Rosario, Violeta and Saurit, Verónica and Appenzeller, Simone and dos Reis Neto, Edgard Torres and Cieza, Jorge and González Naranjo, Luis A and González Bello, Yelitza C and Collado, María Victoria and Sarano, Judith and Retamozo, Soledad and Sattler, María E and Gamboa-Cárdenas, Rocio V and Cairoli, Ernesto and Conti, Silvana M and Amezcua-Guerra, Luis M and Silveira, Luis H and Borba, Eduardo F and Pera, Mariana A and Alba Moreyra, Paula B and Arturi, Valeria and Berbotto, Guillermo A and Gerling, Cristian and Gobbi, Carla A and Gervasoni, Viviana L and Scherbarth, Hugo R and Brenol, João C Tavares and Cavalcanti, Fernando and Costallat, Lilian T Lavras and Da Silva, Nilzio A and Monticielo, Odirlei A and Seguro, Luciana Parente Costa and Xavier, Ricardo M and Llanos, Carolina and Montúfar Guardado, Rubén A and Garcia de la Torre, Ignacio and Pineda, Carlos and Portela Hernández, Margarita and Danza, Alvaro and Guibert-Toledano, Marlene and Reyes, Gil Llerena and Acosta Colman, Maria Isabel and Aquino, Alicia M and Mora-Trujillo, Claudia S and Muñoz-Louis, Roberto and García Valladares, Ignacio and Orozco, María Celeste and Burgos, Paula I and Betancur, Graciela V and Alarcón, Graciela S and Grp Latino Amer Estudio Lupus and Pan-Amer League Assoc Rheumatology and Grupo Latino Americano de Estudio del Lupus (GLADEL) and Pan-American League of Associations of Rheumatology (PANLAR)
Annals of the Rheumatic Diseases, ISSN 0003-4967, 11/2018, Volume 77, Issue 11, pp. 1549 - 1557
Systemic lupus erythematosus (SLE), a complex and heterogeneous autoimmune disease, represents a significant challenge for both diagnosis and treatment.... 
treatment | lupus nephritis | systemic lupus erythematosus | HYDROXYCHLOROQUINE | MORTALITY | MANAGEMENT | INCEPTION COHORT | DISEASE | RISK | RHEUMATOLOGY | 3 ETHNIC-GROUPS | COLLEGE-OF-RHEUMATOLOGY | ANCESTRY | DAMAGE | Lupus | Anticoagulants | Aspirin | Systemic lupus erythematosus | Glucocorticoids | Rheumatology | Socio-economic aspects | Rituximab | Arthritis | Patients | Antiphospholipid syndrome | 1506 | 2311 | Recommendation
Journal Article