A GRASP Algorithm for Clustering. - José Ramón Cano; Oscar Cordón; Francisco Herrera; Luciano Sánchez (IBERAMIA, 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)

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)

Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. - José Ramón Cano; Francisco Herrera; Manuel Lozano 001 (IEEE Trans. Evol. Comput., 2003)

Stratification for scaling up evolutionary prototype selection. - José Ramón Cano; Francisco Herrera; Manuel Lozano 001 (Pattern Recognit. Lett., 2005)

A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems. - Salvador García 001; José Ramón Cano; Alberto Fernández 001; Francisco Herrera (IDEAL, 2006)

Incorporating Knowledge in Evolutionary Prototype Selection. - Salvador García 001; José Ramón Cano; Francisco Herrera (IDEAL, 2006)

On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. - José Ramón Cano; Francisco Herrera; Manuel Lozano 001 (Appl. Soft Comput., 2006)

Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. - José Ramón Cano; Francisco Herrera; Manuel Lozano 001 (Data Knowl. Eng., 2007)

Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection. - José Ramón Cano; Francisco Herrera; Manuel Lozano 001; Salvador García 001 (Expert Syst. Appl., 2008)

Replacement strategies to preserve useful diversity in steady-state genetic algorithms. - Manuel Lozano 001; Francisco Herrera; José Ramón Cano (Inf. Sci., 2008)

A memetic algorithm for evolutionary prototype selection: A scaling up approach. - Salvador García 001; José Ramón Cano; Francisco Herrera (Pattern Recognit., 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 001; Francisco Herrera (Pattern Recognit. Lett., 2008)

Diagnose Effective Evolutionary Prototype Selection Using an Overlapping Measure. - Salvador García 001; José Ramón Cano; Ester Bernadó-Mansilla; Francisco Herrera (Int. J. Pattern Recognit. Artif. Intell., 2009)

A Review on Evolutionary Prototype Selection. - Salvador García 001; José Ramón Cano; Francisco Herrera (Intelligent Systems for Automated Learning and Adaptation, 2010)

Predictive-collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support. - José Ramón Cano (Expert Syst. Appl., 2012)

Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. - Salvador García 001; Joaquín Derrac; José Ramón Cano; Francisco Herrera (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Analysis of data complexity measures for classification. - José Ramón Cano (Expert Syst. Appl., 2013)

A Nearest Hyperrectangle Monotonic Learning Method. - Javier García 004; José Ramón Cano; Salvador García 001 (HAIS, 2016)

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms. - Javier García 004; Adnan Al-bar; Naif R. Aljohani; José Ramón Cano; Salvador García 001 (Int. J. Comput. Intell. Syst., 2016)

Training set selection for monotonic ordinal classification. - José Ramón Cano; Salvador García 001 (Data Knowl. Eng., 2017)

Prototype selection to improve monotonic nearest neighbor. - José Ramón Cano; Naif R. Aljohani; Rabeeh Ayaz Abbasi; Jalal S. Alowibdi; Salvador García 001 (Eng. Appl. Artif. Intell., 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 001; Ilyoung Chong (Int. J. Semantic Web Inf. Syst., 2017)

MoNGEL: monotonic nested generalized exemplar learning. - Javier García 004; Habib M. Fardoun; Daniyal M. Alghazzawi; José Ramón Cano; Salvador García 001 (Pattern Anal. Appl., 2017)

A First Attempt on Monotonic Training Set Selection. - José Ramón Cano; Salvador García 001 (HAIS, 2018)

Credal C4.5 with Refinement of Parameters. - Carlos Javier Mantas; Joaquín Abellán; Javier G. Castellano; José Ramón Cano; Serafín Moral (IPMU (3), 2018)

Monotonic classification: An overview on algorithms, performance measures and data sets. - José Ramón Cano; Pedro Antonio Gutiérrez; Bartosz Krawczyk; Michal Wozniak 001; Salvador García 001 (Neurocomputing, 2019)

Label noise filtering techniques to improve monotonic classification. - José Ramón Cano; Julián Luengo; Salvador García 001 (Neurocomputing, 2019)

Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. - Germán González-Almagro; Alejandro Rosales-Pérez; Julián Luengo; José Ramón Cano; Salvador García 001 (GECCO, 2020)

Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. - Germán González-Almagro; Juan-Luis Suárez; Julián Luengo; José Ramón Cano; Salvador García 001 (HAIS, 2020)

Similarity-based and Iterative Label Noise Filters for Monotonic Classification. - José Ramón Cano; Julián Luengo; Salvador García 001 (HICSS, 2020)

DILS: Constrained clustering through dual iterative local search. - Germán González-Almagro; Julián Luengo; José Ramón Cano; Salvador García 001 (Comput. Oper. Res., 2020)

Enhancing instance-level constrained clustering through differential evolution. - Germán González-Almagro; Julián Luengo; José Ramón Cano; Salvador García 001 (Appl. Soft Comput., 2021)

Decomposition-Fusion for Label Distribution Learning. - Manuel González; Germán González-Almagro; Isaac Triguero; José Ramón Cano; Salvador García 001 (Inf. Fusion, 2021)

Synthetic Sample Generation for Label Distribution Learning. - Manuel González; Julián Luengo; José Ramón Cano; Salvador García 001 (Inf. Sci., 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 001 (Swarm Evol. Comput., 2021)

Monotonic Constrained Clustering: A First Approach. - Germán González-Almagro; Pablo Sánchez Bermejo; Juan-Luis Suárez; José Ramón Cano; Salvador García 001 (IEA/AIE, 2022)

3SHACC: Three stages hybrid agglomerative constrained clustering. - Germán González-Almagro; Juan-Luis Suárez; Julián Luengo; José Ramón Cano; Salvador García 001 (Neurocomputing, 2022)

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 001 (Inf. Fusion, 2024)

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 001 (Artif. Intell. Rev., 2025)