José Ramón Cano de Amo
Contact
jrcano@ujaen.B4X3YD0G@oeEes
Organization
UJA| Total | From 2020: | |
|---|---|---|
| Citas | Total: 2998 | From 2020: 958 |
| Índice H | Total: 21 | From 2020: 14 |
| Índice i10 | Total: 35 | From 2020: 21 |
Papers (59)
| Title | Authors | Year |
|---|---|---|
| Evaluation of the management of Clostridioides difficile infection as a risk factor for recurrence. A retrospective observational study | JMB Allende, I Ureña, L Cañivano, S García, C Paz, A Olmo-Ruiloba, .... | 2024 |
| Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions | G González-Almagro, D Peralta, E De Poorter, JR Cano, S García. | 2023 |
| Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints | G González-Almagro, JL Suárez, P Sánchez-Bermejo, JR Cano, S García. | 2023 |
| Monotonic Constrained Clustering: A First Approach | G González-Almagro, PS Bermejo, JL Suarez, JR Cano, S García. | 2022 |
| Enhancing instance-level constrained clustering through differential evolution | G González-Almagro, J Luengo, JR Cano, S García. | 2021 |
| ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism | G González-Almagro, A Rosales-Pérez, J Luengo, JR Cano, S García. | 2021 |
| 3SHACC: Three Stages Hybrid Agglomerative Constrained Clustering | G González-Almagro, JL Suarez, J Luengo, JR Cano, S García. | 2021 |
| Similarity-based and Iterative Label Noise Filters for Monotonic Classification | JR Cano, J Luengo, S García. | 2020 |
| DILS: constrained clustering through Dual Iterative Local Search | G González-Almagro, J Luengo, JR Cano, S García. | 2020 |
| ProLSFEO-LDL: Prototype Selection and Label-Specific Feature Evolutionary Optimization for Label Distribution Learning | M González, JR Cano, S García. | 2020 |
| Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism | G González-Almagro, A Rosales-Pérez, J Luengo, JR Cano, S García. | 2020 |
| Synthetic Sample Generation for Label Distribution Learning | M González, J Luengo, JR Cano, S García. | 2020 |
| Decomposition-Fusion for Label Distribution Learning | M González, G González-Almagro, I Triguero, JR Cano, S García. | 2020 |
| Agglomerative Constrained Clustering Through Similarity and Distance Recalculation | G González-Almagro, JL Suarez, J Luengo, JR Cano, S García. | 2020 |
| Similarity-based and Iterative Label Noise Filters for Monotonic Classification. | JR Cano, J Luengo, S García. | 2020 |
| Label noise filtering techniques to improve monotonic classification | JR Cano, J Luengo, S García. | 2019 |
| Monotonic classification: An overview on algorithms, performance measures and data sets | JR Cano, PA Gutiérrez, B Krawczyk, M Woźniak, S García. | 2019 |
| A First Attempt on Monotonic Training Set Selection | JR Cano, S García. | 2018 |
| Credal C4. 5 with Refinement of Parameters | CJ Mantas, J Abellán, JG Castellano, JR Cano, S Moral. | 2018 |
| CommuniMents: A framework for detecting community based sentiments for events | MA Jarwar, RA Abbasi, M Mushtaq, O Maqbool, NR Aljohani, A Daud, .... | 2017 |
| Prototype selection to improve monotonic nearest neighbor | JR Cano, NR Aljohani, RA Abbasi, JS Alowidbi, S Garcia. | 2017 |
| MoNGEL: monotonic nested generalized exemplar learning | J García, HM Fardoun, DM Alghazzawi, JR Cano, S García. | 2017 |
| Training set selection for monotonic ordinal classification | JR Cano, S García. | 2017 |
| Hyperrectangles selection for monotonic classification by using evolutionary algorithms | J García, AM AlBar, NR Aljohani, JR Cano, S García. | 2016 |
| A Nearest Hyperrectangle Monotonic Learning Method | J García, JR Cano, S García. | 2016 |
| A Nearest Hyperrectangle Monotonic | J García¹, JR Cano, S García. | 2016 |
| Analysis of data complexity measures for classification | JR Cano. | 2013 |
| Expert Systems with Applic ations | JR Cano. | 2013 |
| Predictive–collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support | JR Cano. | 2012 |
| Prototype selection for nearest neighbor classification: Taxonomy and empirical study | S Garcia, J Derrac, JR Cano, F Herrera. | 2011 |
| Prototype selection for nearest neighbor classification: Survey of methods | S Garcıa, J Derrac, JR Cano, F Herrera. | 2010 |
| A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency | S García, JR Cano, F Herrera. | 2010 |
| Modelo predictivo colaborativo de apoyo al diagnostico en servicio de urgencias psiquiatricas | JR Cano, MJ del Jesús, P González, JJ Aguilera, AG López, F Herrera, .... | 2009 |
| Diagnose effective evolutionary prototype selection using an overlapping measure | S Garcia, JR Cano, E Bernado-Mansilla, F Herrera. | 2009 |
| Expert systems with applications | F Yang, T Sun, C Zhang. | 2009 |
| A memetic algorithm for evolutionary prototype selection: A scaling up approach | S García, JR Cano, F Herrera. | 2008 |
| Replacement strategies to preserve useful diversity in steady-state genetic algorithms | M Lozano, F Herrera, JR Cano. | 2008 |
| Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes | JR Cano, S García, F Herrera. | 2008 |
| Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection | JR Cano, F Herrera, M Lozano, S García. | 2008 |
| Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability | JR Cano, F Herrera, M Lozano. | 2007 |
| Un algoritmo memético para selección de prototipos: Una propuesta eficiente para problemas de tamano medio | S Garcıa, JR Cano, F Herrera. | 2007 |
| Analysis of evolutionary prototipe selection by means of a data complexity measure based on class separability | JR Cano, S García, F Herrera, EB Mansilla. | 2007 |
| On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining | JR Cano, F Herrera, M Lozano. | 2006 |
| A proposal of evolutionary prototype selection for class imbalance problems | S García, JR Cano, A Fernández, F Herrera. | 2006 |
| Técnicas de reducción de datos en KDD. El uso de Algoritmos Evolutivos para la Selección de Instancias | F Herrera, JR Cano. | 2006 |
| Incorporating knowledge in evolutionary prototype selection | S García, JR Cano, F Herrera. | 2006 |
| Técnicas de reducción de datos en KDD | F Herrera, J Cano. | 2006 |
| 2. Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining | JR Cano, F Herrera, M Lozano. | 2006 |
| Stratification for scaling up evolutionary prototype selection | JR Cano, F Herrera, M Lozano. | 2005 |
| Replacement strategies to maintain useful diversity in steady-state genetic algorithms | M Lozano, F Herrera, JR Cano. | 2005 |
| A study on the combination of evolutionary algorithms and stratified strategies for training set selection in data mining | JR Cano, F Herrera, M Lozano. | 2005 |
| Strategies for scaling up evolutionary instance reduction algorithms for data mining | JR Cano, F Herrera, M Lozano. | 2005 |
| Instance selection using evolutionary algorithms: an experimental study | JR Cano, F Herrera, M Lozano. | 2005 |
| Extracción de modelos predictivos e interpretables en conjuntos de datos de tamaño grande mediante la selección de conjuntos de entrenamiento | JR Cano, F Herrera, M Lozano. | 2005 |
| Extración de modelos predictivos e interpretables en conjuntos de datos tamaño grande mediante la selección de conjuntos de entrenamiento | R Cano, F Herrera, M Lozano. | 2005 |
| Insomnio: enfoque diagnòstico y terapéutico | J Cano, J Garcìa. | 2005 |
| Reducción de datos basada en Selección Evolutiva de Instancias para Minerıa de Datos | JRC de Amo. | 2004 |
| Evolutionary stratified instance selection applied to training set selection for extracting high precise-interpretable classification rules | JR Cano, F Herrera, M Lozano. | 2004 |
| Seleccion Evolutiva Estratificada de Conjuntos de Entrenamiento para la Obtención de Bases de Reglas con un Alto Equilibrio entre Precisión e Interpretabilidad | JR Cano, F Herrera, M Lozano. | 2004 |
| Taxonomy and significance of black aspergilli. | M Lourdes Abarca, F Accensi, J Cano, F Javier Cabañes. | 2004 |
| Linguistic modeling with hierarchical systems of weighted linguistic rules | R Alcalá, JR Cano, O Cordón, F Herrera, P Villar, I Zwir. | 2003 |
| Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study | JR Cano, F Herrera, M Lozano. | 2003 |
| A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure | JR Cano, O Cordón, F Herrera, L Sánchez. | 2002 |
| A GRASP algorithm for clustering | JR Cano, O Cordón, F Herrera, L Sánchez. | 2002 |
| Tumor necrosis factor-alpha: a mediator in the pathogenesis of cardiac insufficiency | EG Herrera, AG Cubillos, SJ Stetson, RN Cano, FF Herrera, JB Durand, .... | 1999 |
| Heterotopic heart transplantation: 13-year experience at the Methodist Hospital of the Baylor Medical College | EG Herrera, GP Noon, JB Durand, SJ Stetson, S Zylicz, L Johnson, .... | 1999 |
| Preliminary results in the study of chromosome races found in Ameles abjecta (Amelinae, Mantodea) | JC Orozco, M Espejo, J Cano. | 1983 |
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