José Ramón Cano

According to our database, José Ramón Cano authored at least 40 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Semi-supervised clustering with two types of background knowledge: Fusing pairwise constraints and monotonicity constraints.

Germán González-Almagro, Pablo Sánchez Bermejo, Juan-Luis Suárez, José Ramón Cano, Salvador García

Inf. Fusion, February, 2024

2023

Semi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions.

Germán González-Almagro, Daniel Peralta, Eli De Poorter, José Ramón Cano, Salvador García

CoRR, 2023

2022

3SHACC: Three stages hybrid agglomerative constrained clustering.

Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García

Neurocomputing, 2022

Monotonic Constrained Clustering: A First Approach.

Germán González-Almagro, Pablo Sánchez Bermejo, Juan-Luis Suárez, José Ramón Cano, Salvador García

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, 2022

2021

ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism.

Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García

Swarm Evol. Comput., 2021

Synthetic Sample Generation for Label Distribution Learning.

Manuel González, Julián Luengo, José Ramón Cano, Salvador García

Inf. Sci., 2021

Decomposition-Fusion for Label Distribution Learning.

Manuel González, Germán González-Almagro, Isaac Triguero, José Ramón Cano, Salvador García

Inf. Fusion, 2021

Enhancing instance-level constrained clustering through differential evolution.

Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García

Appl. Soft Comput., 2021

2020

DILS: Constrained clustering through dual iterative local search.

Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García

Comput. Oper. Res., 2020

Similarity-based and Iterative Label Noise Filters for Monotonic Classification.

José Ramón Cano, Julián Luengo, Salvador García

Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020

Agglomerative Constrained Clustering Through Similarity and Distance Recalculation.

Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García

Proceedings of the Hybrid Artificial Intelligent Systems - 15th International Conference, 2020

Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism.

Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

Label noise filtering techniques to improve monotonic classification.

José Ramón Cano, Julián Luengo, Salvador García

Neurocomputing, 2019

Monotonic classification: An overview on algorithms, performance measures and data sets.

José Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michal Wozniak, Salvador García

Neurocomputing, 2019

2018

Credal C4.5 with Refinement of Parameters.

Carlos Javier Mantas, Joaquín Abellán, Javier G. Castellano, José Ramón Cano, Serafín Moral

Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2018

A First Attempt on Monotonic Training Set Selection.

José Ramón Cano, Salvador García

Proceedings of the Hybrid Artificial Intelligent Systems - 13th International Conference, 2018

2017

MoNGEL: monotonic nested generalized exemplar learning.

Javier García, Habib M. Fardoun, Daniyal M. Alghazzawi, José Ramón Cano, Salvador García

Pattern Anal. Appl., 2017

CommuniMents: A Framework for Detecting Community Based Sentiments for Events.

Muhammad Aslam Jarwar, Rabeeh Ayaz Abbasi, Mubashar Mushtaq, Onaiza Maqbool, Naif R. Aljohani, Ali Daud, Jalal S. Alowibdi, José Ramón Cano, Salvador García, Ilyoung Chong

Int. J. Semantic Web Inf. Syst., 2017

Prototype selection to improve monotonic nearest neighbor.

José Ramón Cano, Naif R. Aljohani, Rabeeh Ayaz Abbasi, Jalal S. Alowibdi, Salvador García

Eng. Appl. Artif. Intell., 2017

Training set selection for monotonic ordinal classification.

José Ramón Cano, Salvador García

Data Knowl. Eng., 2017

2016

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms.

Javier García, Adnan Al-bar, Naif R. Aljohani, José Ramón Cano, Salvador García

Int. J. Comput. Intell. Syst., 2016

A Nearest Hyperrectangle Monotonic Learning Method.

Javier García, José Ramón Cano, Salvador García

Proceedings of the Hybrid Artificial Intelligent Systems - 11th International Conference, 2016

2013

Analysis of data complexity measures for classification.

José Ramón Cano

Expert Syst. Appl., 2013

2012

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study.

Salvador García, Joaquín Derrac, José Ramón Cano, Francisco Herrera

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Predictive-collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support.

José Ramón Cano

Expert Syst. Appl., 2012

2010

A Review on Evolutionary Prototype Selection.

Salvador García, José Ramón Cano, Francisco Herrera

Proceedings of the Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, 2010

2009

Diagnose Effective Evolutionary Prototype Selection Using an Overlapping Measure.

Salvador García, José Ramón Cano, Ester Bernadó-Mansilla, Francisco Herrera

Int. J. Pattern Recognit. Artif. Intell., 2009

2008

Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes.

José Ramón Cano, Salvador García, Francisco Herrera

Pattern Recognit. Lett., 2008

A memetic algorithm for evolutionary prototype selection: A scaling up approach.

Salvador García, José Ramón Cano, Francisco Herrera

Pattern Recognit., 2008

Replacement strategies to preserve useful diversity in steady-state genetic algorithms.

Manuel Lozano, Francisco Herrera, José Ramón Cano

Inf. Sci., 2008

Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection.

José Ramón Cano, Francisco Herrera, Manuel Lozano, Salvador García

Expert Syst. Appl., 2008

2007

Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability.

José Ramón Cano, Francisco Herrera, Manuel Lozano

Data Knowl. Eng., 2007

2006

On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining.

José Ramón Cano, Francisco Herrera, Manuel Lozano

Appl. Soft Comput., 2006

Incorporating Knowledge in Evolutionary Prototype Selection.

Salvador García, José Ramón Cano, Francisco Herrera

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems.

Salvador García, José Ramón Cano, Alberto Fernández, Francisco Herrera

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

2005

Stratification for scaling up evolutionary prototype selection.

José Ramón Cano, Francisco Herrera, Manuel Lozano

Pattern Recognit. Lett., 2005

2003

Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study.

José Ramón Cano, Francisco Herrera, Manuel Lozano

IEEE Trans. Evol. Comput., 2003

Linguistic modeling with hierarchical systems of weighted linguistic rules.

Rafael Alcalá, José Ramón Cano, Oscar Cordón, Francisco Herrera, Pedro Villar, Igor Zwir

Int. J. Approx. Reason., 2003

2002

A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure.

José Ramón Cano, Oscar Cordón, Francisco Herrera, Luciano Sánchez

J. Intell. Fuzzy Syst., 2002

A GRASP Algorithm for Clustering.

José Ramón Cano, Oscar Cordón, Francisco Herrera, Luciano Sánchez

Proceedings of the Advances in Artificial Intelligence, 2002