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ORCIDJosé Cristóbal Riquelme Santos
José C. Riquelme
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- affiliation: University of Seville, Spain
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2020 – today
- 2022
[j68]Manuel Carranza-García, F. Javier Galán-Sales, José María Luna-Romera, José C. Riquelme:
Object detection using depth completion and camera-LiDAR fusion for autonomous driving. Integr. Comput. Aided Eng. 29(3): 241-258 (2022)- [c108]Tomás Cabello-López
, Manuel Cañizares-Juan, Manuel Carranza-García, Jorge García-Gutiérrez, José C. Riquelme:
Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation. HAIS 2022: 133-140 - 2021
- [j67]Pedro Lara-Benítez, Manuel Carranza-García, José C. Riquelme:
An Experimental Review on Deep Learning Architectures for Time Series Forecasting. Int. J. Neural Syst. 31(3): 2130001:1-2130001:28 (2021) - [j66]Manuel Carranza-García, Pedro Lara-Benítez, Jorge García-Gutiérrez, José C. Riquelme:
Enhancing object detection for autonomous driving by optimizing anchor generation and addressing class imbalance. Neurocomputing 449: 229-244 (2021) - [j65]Belén Vega-Márquez, Isabel A. Nepomuceno-Chamorro, Cristina Rubio-Escudero, José C. Riquelme:
OCEAn: Ordinal classification with an ensemble approach. Inf. Sci. 580: 221-242 (2021) - [c107]Manuel Carranza-García, Pedro Lara-Benítez, José María Luna-Romera, José C. Riquelme:
Feature Selection on Spatio-Temporal Data for Solar Irradiance Forecasting. SOCO 2021: 654-664 - [c106]José María Luna-Romera, Manuel Carranza-García, David Gutiérrez-Avilés, José Cristóbal Riquelme Santos:
Study Case of Household Electricity Consumption Patterns in London by Clustering Methodology. SOCO 2021: 706-716 - [e3]Enrique Alba, Gabriel Luque, Francisco Chicano, Carlos Cotta, David Camacho, Manuel Ojeda-Aciego, Susana Montes, Alicia Troncoso, José C. Riquelme, Rodrigo Gil-Merino:
Advances in Artificial Intelligence - 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020/2021, Málaga, Spain, September 22-24, 2021, Proceedings. Lecture Notes in Computer Science 12882, Springer 2021, ISBN 978-3-030-85712-7 [contents] - [i4]Pedro Lara-Benítez, Manuel Carranza-García, José C. Riquelme:
An Experimental Review on Deep Learning Architectures for Time Series Forecasting. CoRR abs/2103.12057 (2021) - [i3]Manuel Carranza-García, Pedro Lara-Benítez, Jorge García-Gutiérrez, José C. Riquelme:
Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance. CoRR abs/2104.03888 (2021) - 2020
- [j64]Laura Macías-García, María Martínez-Ballesteros, José María Luna-Romera, José Manuel García-Heredia, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance. Artif. Intell. Medicine 110: 101976 (2020) - [j63]Francisco Martínez-Álvarez, Gualberto Asencio-Cortés, José F. Torres, David Gutiérrez-Avilés, Laura Melgar-García, Rubén Pérez-Chacón, Cristina Rubio-Escudero, José C. Riquelme, Alicia Troncoso Lora:
Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model. Big Data 8(4): 308-322 (2020) - [j62]Pedro Lara-Benítez, Manuel Carranza-García, Jorge García-Gutiérrez, José C. Riquelme:
Asynchronous dual-pipeline deep learning framework for online data stream classification. Integr. Comput. Aided Eng. 27(2): 101-119 (2020) - [j61]Antonio J. Tallón-Ballesteros, José C. Riquelme, Roberto Ruiz:
Filter-based feature selection in the context of evolutionary neural networks in supervised machine learning. Pattern Anal. Appl. 23(1): 467-491 (2020) - [c105]Pedro Lara-Benítez, Manuel Carranza-García, Francisco Martínez-Álvarez, José C. Riquelme:
On the Performance of Deep Learning Models for Time Series Classification in Streaming. SOCO 2020: 144-154 - [c104]Miguel Ángel Molina, Gualberto Asencio-Cortés, José C. Riquelme, Francisco Martínez-Álvarez:
A Preliminary Study on Deep Transfer Learning Applied to Image Classification for Small Datasets. SOCO 2020: 741-750 - [i2]Pedro Lara-Benítez, Manuel Carranza-García, Francisco Martínez-Álvarez, José C. Riquelme:
On the performance of deep learning models for time series classification in streaming. CoRR abs/2003.02544 (2020) - [i1]Francisco Martínez-Álvarez, Gualberto Asencio-Cortés, José F. Torres, David Gutiérrez-Avilés, Laura Melgar-García, Rubén Pérez-Chacón, Cristina Rubio-Escudero, José C. Riquelme, Alicia Troncoso:
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model. CoRR abs/2003.13633 (2020)
2010 – 2019
- 2019
- [j60]Catalina Gomez-Quiles, Gualberto Asencio-Cortés, Adolfo Gastalver-Rubio, Francisco Martínez-Álvarez, Alicia Troncoso, Joan Manresa, José C. Riquelme, Jesús Manuel Riquelme-Santos:
A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System. IEEE Access 7: 120840-120856 (2019) - [j59]José María Luna-Romera, Fernando Núñez-Hernández, María Martínez-Ballesteros, José C. Riquelme, Carlos Usabiaga:
Analysis of the Evolution of the Spanish Labour Market Through Unsupervised Learning. IEEE Access 7: 121695-121708 (2019) - [j58]Daniel Mateos-García, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule. Neurocomputing 326-327: 54-60 (2019) - [j57]Antonio J. Tallón-Ballesteros, José C. Riquelme, Roberto Ruiz:
Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems. Neurocomputing 353: 28-44 (2019) - [j56]José María Luna-Romera, María Martínez-Ballesteros, Jorge García-Gutiérrez, José C. Riquelme:
External clustering validity index based on chi-squared statistical test. Inf. Sci. 487: 1-17 (2019) - [j55]Manuel Carranza-García, Jorge García-Gutiérrez, José C. Riquelme:
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks. Remote. Sens. 11(3): 274 (2019) - [c103]José David Martín-Fernández, José María Luna-Romera, Beatriz Pontes, José Cristóbal Riquelme Santos:
Indexes to Find the Optimal Number of Clusters in a Hierarchical Clustering. SOCO 2019: 3-13 - [c102]Belén Vega-Márquez, Cristina Rubio-Escudero, José C. Riquelme, Isabel A. Nepomuceno-Chamorro:
Creation of Synthetic Data with Conditional Generative Adversarial Networks. SOCO 2019: 231-240 - 2018
- [j54]Álvaro Gómez-Losada, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, José C. Riquelme:
A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information. Environ. Model. Softw. 110: 52-61 (2018) - [j53]Diana Martín, María Martínez-Ballesteros, Diego García-Gil, Jesús Alcalá-Fdez, Francisco Herrera, José Cristóbal Riquelme Santos:
MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems. Knowl. Based Syst. 153: 176-192 (2018) - [j52]José María Luna-Romera, Jorge García-Gutiérrez, María Martínez-Ballesteros, José Cristóbal Riquelme Santos:
An approach to validity indices for clustering techniques in Big Data. Prog. Artif. Intell. 7(2): 81-94 (2018) - [c101]David Gutiérrez-Avilés, J. A. Fábregas, J. Tejedor, Francisco Martínez-Álvarez, Alicia Troncoso, A. Arcos, José C. Riquelme:
SmartFD: A Real Big Data Application for Electrical Fraud Detection. HAIS 2018: 120-130 - [c100]Cristina Rubio-Escudero, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Alicia Troncoso, José C. Riquelme:
Impact of Auto-evaluation Tests as Part of the Continuous Evaluation in Programming Courses. SOCO-CISIS-ICEUTE 2018: 553-561 - 2017
- [j51]Francisco Martínez-Álvarez, Alicia Troncoso, Jorge Reyes, María Martínez-Ballesteros, José C. Riquelme:
Applications of Computational Intelligence in Time Series. Comput. Intell. Neurosci. 2017: 9361749:1-9361749:2 (2017) - [j50]María Martínez-Ballesteros, José Manuel García-Heredia, Isabel A. Nepomuceno-Chamorro, José Cristóbal Riquelme Santos:
Machine learning techniques to discover genes with potential prognosis role in Alzheimer's disease using different biological sources. Inf. Fusion 36: 114-129 (2017) - [j49]Laura Macías-García, José María Luna-Romera, Jorge García-Gutiérrez, María Martínez-Ballesteros, José Cristóbal Riquelme Santos, Ricardo González-Cámpora:
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation. J. Biomed. Informatics 72: 33-44 (2017) - [j48]Amparo Alonso-Betanzos, José A. Gámez, Francisco Herrera, José Miguel Puerta, José C. Riquelme:
Volume, variety and velocity in Data Science. Knowl. Based Syst. 117: 1-2 (2017) - [c99]Antonio J. Tallón-Ballesteros, José C. Riquelme:
Low Dimensionality or Same Subsets as a Result of Feature Selection: An In-Depth Roadmap. IWINAC (2) 2017: 531-539 - 2016
- [j47]Antonio J. Tallón-Ballesteros, José Cristóbal Riquelme Santos, Roberto Ruiz Sánchez:
Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Connect. Sci. 28(3): 242-257 (2016) - [j46]Daniel Mateos-García, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
An evolutionary voting for k-nearest neighbours. Expert Syst. Appl. 43: 9-14 (2016) - [j45]María Martínez-Ballesteros, Alicia Troncoso, Francisco Martínez-Álvarez, José C. Riquelme:
Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176: 36-47 (2016) - [j44]María Martínez-Ballesteros, Alicia Troncoso Lora, Francisco Martínez-Álvarez, José C. Riquelme:
Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowl. Inf. Syst. 49(2): 481-509 (2016) - [c98]José María Luna-Romera, María del Mar Martínez-Ballesteros, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark. CAEPIA 2016: 160-169 - [c97]Antonio J. Tallón-Ballesteros, José C. Riquelme, Roberto Ruiz Sánchez:
Accuracy Increase on Evolving Product Unit Neural Networks via Feature Subset Selection. HAIS 2016: 136-148 - [c96]Jorge García-Gutiérrez, Eduardo González-Ferreiro, Daniel Mateos-García, José Cristóbal Riquelme Santos:
A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation. HAIS 2016: 588-596 - 2015
- [j43]María Martínez-Ballesteros, Jaume Bacardit, Alicia Troncoso Lora, José C. Riquelme:
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets. Integr. Comput. Aided Eng. 22(1): 21-39 (2015) - [j42]Jorge García-Gutiérrez, Daniel Mateos-García, Mariano García, José Cristóbal Riquelme Santos:
An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion. Neurocomputing 163: 17-24 (2015) - [j41]Alicia Troncoso Lora, Marta Arias, José C. Riquelme:
A multi-scale smoothing kernel for measuring time-series similarity. Neurocomputing 167: 8-17 (2015) - [j40]Jorge García-Gutiérrez, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables. Neurocomputing 167: 24-31 (2015) - [c95]Antonio J. Tallón-Ballesteros, José C. Riquelme:
Data Cleansing Meets Feature Selection: A Supervised Machine Learning Approach. IWINAC (2) 2015: 369-378 - 2014
- [j39]Jorge García-Gutiérrez, Eduardo González-Ferreiro, José Cristóbal Riquelme Santos, David Miranda, Ulises Diéguez-Aranda, Rafael M. Navarro-Cerrillo:
Evolutionary feature selection to estimate forest stand variables using LiDAR. Int. J. Appl. Earth Obs. Geoinformation 26: 119-131 (2014) - [j38]María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
Selecting the best measures to discover quantitative association rules. Neurocomputing 126: 3-14 (2014) - [j37]David Gutiérrez-Avilés, Cristina Rubio-Escudero, Francisco Martínez-Álvarez, José C. Riquelme:
TriGen: A genetic algorithm to mine triclusters in temporal gene expression data. Neurocomputing 132: 42-53 (2014) - [j36]María Martínez-Ballesteros, Isabel A. Nepomuceno-Chamorro, José C. Riquelme:
Discovering gene association networks by multi-objective evolutionary quantitative association rules. J. Comput. Syst. Sci. 80(1): 118-136 (2014) - [c94]Daniel Rodríguez, Israel Herraiz, Rachel Harrison, José Javier Dolado, José C. Riquelme:
Preliminary comparison of techniques for dealing with imbalance in software defect prediction. EASE 2014: 43:1-43:10 - [c93]Jorge García-Gutiérrez, Daniel Mateos-García, José Cristóbal Riquelme Santos:
Improving the k-Nearest Neighbour Rule by an Evolutionary Voting Approach. HAIS 2014: 296-305 - [c92]Antonio J. Tallón-Ballesteros, José C. Riquelme:
Tackling Ant Colony Optimization Meta-Heuristic as Search Method in Feature Subset Selection Based on Correlation or Consistency Measures. IDEAL 2014: 386-393 - [c91]Antonio J. Tallón-Ballesteros, José Cristóbal Riquelme Santos:
Deleting or keeping outliers for classifier training? NaBIC 2014: 281-286 - [p2]Antonio J. Tallón-Ballesteros, José C. Riquelme:
Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning. Computational Intelligence in Digital Forensics 2014: 413-428 - 2013
- [j35]Antonio J. Tallón-Ballesteros, César Hervás-Martínez, José C. Riquelme, Roberto Ruiz:
Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems. Neurocomputing 114: 107-117 (2013) - [j34]Daniel Rodríguez, Roberto Ruiz, José C. Riquelme, Rachel Harrison:
A study of subgroup discovery approaches for defect prediction. Inf. Softw. Technol. 55(10): 1810-1822 (2013) - [c90]María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
ra A Sensitivity Analysis for Quality Measures of Quantitative Association Rules. HAIS 2013: 578-587 - [c89]Jorge García-Gutiérrez, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
A Comparative Study of Machine Learning Regression Methods on LiDAR Data: A Case Study. SOCO-CISIS-ICEUTE 2013: 249-258 - 2012
- [j33]Francisco Fernández-Navarro, César Hervás-Martínez, Roberto Ruiz, José C. Riquelme:
Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection. Appl. Soft Comput. 12(6): 1787-1800 (2012) - [j32]Roberto Ruiz, José C. Riquelme, Jesús S. Aguilar-Ruiz, Miguel García-Torres:
Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches. Expert Syst. Appl. 39(12): 11094-11102 (2012) - [j31]Jorge García-Gutiérrez, Daniel Mateos-García, José Cristóbal Riquelme Santos:
EVOR-STACK: A label-dependent evolutive stacking on remote sensing data fusion. Neurocomputing 75(1): 115-122 (2012) - [j30]Daniel Rodríguez, Roberto Ruiz, José C. Riquelme, Jesús S. Aguilar-Ruiz:
Searching for rules to detect defective modules: A subgroup discovery approach. Inf. Sci. 191: 14-30 (2012) - [j29]Daniel Mateos-García, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
On the evolutionary optimization of k-NN by label-dependent feature weighting. Pattern Recognit. Lett. 33(16): 2232-2238 (2012) - [c88]Jorge García-Gutiérrez, Daniel Mateos-García, José Cristóbal Riquelme Santos:
A Non-parametric Approach for Accurate Contextual Classification of LIDAR and Imagery Data Fusion. HAIS (2) 2012: 455-466 - [c87]Marta Arias, Alicia Troncoso Lora, José C. Riquelme:
A Kernel for Time Series Classification: Application to Atmospheric Pollutants. SOCO 2012: 417-426 - 2011
- [j28]Jorge García-Gutiérrez, Luis Gonçalves-Seco, José Cristóbal Riquelme Santos:
Automatic environmental quality assessment for mixed-land zones using lidar and intelligent techniques. Expert Syst. Appl. 38(6): 6805-6813 (2011) - [j27]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme, Jesús S. Aguilar-Ruiz:
Discovery of motifs to forecast outlier occurrence in time series. Pattern Recognit. Lett. 32(12): 1652-1665 (2011) - [j26]María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Comput. 15(10): 2065-2084 (2011) - [j25]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme, Jesús S. Aguilar-Ruiz:
Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Trans. Knowl. Data Eng. 23(8): 1230-1243 (2011) - [c86]David Gutiérrez-Avilés, Cristina Rubio-Escudero, José C. Riquelme:
Unravelling the Yeast Cell Cycle Using the TriGen Algorithm. CAEPIA 2011: 155-163 - [c85]Daniel Rodríguez, Mercedes Ruiz Carreira, José C. Riquelme, Rachel Harrison:
Multiobjective simulation optimisation in software project management. GECCO 2011: 1883-1890 - [c84]Francisco Martínez-Álvarez, Alicia Troncoso Lora, A. Morales-Esteban, José C. Riquelme:
Computational Intelligence Techniques for Predicting Earthquakes. HAIS (2) 2011: 287-294 - [c83]Jorge García-Gutiérrez, Eduardo González-Ferreiro, Daniel Mateos-García, José Cristóbal Riquelme Santos, David Miranda:
A Comparative Study between Two Regression Methods on LiDAR Data: A Case Study. HAIS (2) 2011: 311-318 - [c82]María Martínez-Ballesteros, José Cristóbal Riquelme Santos:
Analysis of Measures of Quantitative Association Rules. HAIS (2) 2011: 319-326 - [c81]María Martínez-Ballesteros, Cristina Rubio-Escudero, José C. Riquelme, Francisco Martínez-Álvarez:
Mining Quantitative Association Rules in Microarray Data using Evolutive Algorithms. ICAART (1) 2011: 574-577 - [c80]David Gutiérrez-Avilés, Cristina Rubio-Escudero, José C. Riquelme:
Triclustering on temporary microarray data using the TriGen algorithm. ISDA 2011: 877-881 - [c79]Cristina Rubio-Escudero, Francisco Martínez-Álvarez, María Martínez-Ballesteros, José C. Riquelme:
On the use of algorithms to discover motifs in DNA sequences. ISDA 2011: 1074-1079 - [c78]María Martínez-Ballesteros, Isabel A. Nepomuceno-Chamorro, José C. Riquelme:
Inferring gene-gene associations from Quantitative Association Rules. ISDA 2011: 1241-1246 - [c77]Antonio J. Tallón-Ballesteros, César Hervás-Martínez, José C. Riquelme, Roberto Ruiz:
Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection. IWINAC (2) 2011: 381-390 - [c76]David Gutiérrez-Avilés, Cristina Rubio-Escudero, José C. Riquelme:
Revisiting the yeast cell cycle problem with the improved TriGen algorithm. NaBIC 2011: 515-520 - [c75]Daniel Rodríguez, Roberto Ruiz, José C. Riquelme, Rachel Harrison:
Subgroup Discovery for Defect Prediction. SSBSE 2011: 269-270 - 2010
- [j24]Isabel A. Nepomuceno-Chamorro, Jesús S. Aguilar-Ruiz, José C. Riquelme:
Inferring gene regression networks with model trees. BMC Bioinform. 11: 517 (2010) - [j23]María Martínez-Ballesteros, Alicia Troncoso Lora, Francisco Martínez-Álvarez, José C. Riquelme:
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integr. Comput. Aided Eng. 17(3): 227-242 (2010) - [c74]Daniel Mateos-García, Jorge García-Gutiérrez, José Cristóbal Riquelme Santos:
Label Dependent Evolutionary Feature Weighting for Remote Sensing Data. HAIS (2) 2010: 272-279 - [c73]Jorge García-Gutiérrez, Daniel Mateos-García, José Cristóbal Riquelme Santos:
A SVM and k-NN Restricted Stacking to Improve Land Use and Land Cover Classification. HAIS (2) 2010: 493-500 - [c72]Francisco Fernández-Navarro, César Hervás-Martínez, Pedro Antonio Gutiérrez, Roberto Ruiz, José C. Riquelme:
Evolutionary q-Gaussian Radial Basis Functions for Improving Prediction Accuracy of Gene Classification Using Feature Selection. ICANN (1) 2010: 327-336 - [c71]Jorge García-Gutiérrez, Francisco Martínez-Álvarez, José C. Riquelme:
Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop Land Cover Maps. IEA/AIE (1) 2010: 378-387 - [c70]Daniel Rodríguez, José Javier Dolado, José C. Riquelme, Roberto Ruiz, Miguel-Ángel Sicilia:
Knowledge representation and applied decision making (KREAM). ICCS 2010: 2271
2000 – 2009
- 2009
- [c69]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences. IDA 2009: 357-368 - [c68]María Martínez-Ballesteros, Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme:
Quantitative Association Rules Applied to Climatological Time Series Forecasting. IDEAL 2009: 284-291 - [c67]José Cristóbal Riquelme Santos, Roberto Ruiz, Daniel Rodríguez:
Apoyo a la Decisión en Ingeniería del Software (ADIS, 9ª edición). JISBD 2009: 426-426 - [p1]José Javier Dolado, Daniel Rodríguez-García, José Cristóbal Riquelme Santos, Francisco J. Ferrer-Troyano, Juan J. Cuadrado:
A Two-Stage Zone Regression Method for Global Characterization of a Project Database. Database Technologies: Concepts, Methodologies, Tools, and Applications 2009: 2000-2009 - 2008
- [j22]Alicia Troncoso Lora, José Cristóbal Riquelme Santos, Jesús S. Aguilar-Ruiz, Jesús Manuel Riquelme-Santos:
Evolutionary techniques applied to the optimal short-term scheduling of the electrical energy production. Eur. J. Oper. Res. 185(3): 1114-1127 (2008) - [c66]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José C. Riquelme, Jesús S. Aguilar-Ruiz:
LBF: A Labeled-Based Forecasting Algorithm and Its Application to Electricity Price Time Series. ICDM 2008: 453-461 - [c65]José C. Riquelme, Roberto Ruiz, Daniel Rodríguez:
Identificación de Fallos en Módulos Software. JISBD 2008: 195-204 - [c64]Roberto Ruiz, José C. Riquelme, Jesús S. Aguilar-Ruiz:
Best Agglomerative Ranked Subset for Feature Selection. FSDM 2008: 148-162 - 2007
- [j21]Francisco J. Ferrer-Troyano, Jesús S. Aguilar-Ruiz, José C. Riquelme:
FACIL - An approach for classifying data streams by decision rules and border examples. Monde des Util. Anal. Données 36: 92-101 (2007) - [j20]Jesús S. Aguilar-Ruiz, Raúl Giráldez, José Cristóbal Riquelme Santos:
Natural Encoding for Evolutionary Supervised Learning. IEEE Trans. Evol. Comput. 11(4): 466-479 (2007) - [c63]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José Cristóbal Riquelme Santos, Jesús S. Aguilar-Ruiz:
Detection of Microcalcifications in Mammographies Based on Linear Pixel Prediction and Support-Vector Machines. CBMS 2007: 141-146 - [c62]Francisco Martínez-Álvarez, Alicia Troncoso Lora, José Cristóbal Riquelme Santos, Jesús Manuel Riquelme-Santos:
Partitioning-Clustering Techniques Applied to the Electricity Price Time Series. IDEAL 2007: 990-999 - 2006
- [j19]Roberto Ruiz, Karina Gilbert, José C. Riquelme:
Special Issue: Data Mining. Inteligencia Artif. 10(29): 7-9 (2006) - [j18]José C. Riquelme, Roberto Ruiz, Karina Gilbert:
Data Mining: Concepts and Trends. Inteligencia Artif. 10(29): 11-18 (2006) - [j17]José Cristóbal Riquelme Santos, Macario Polo, Jesús S. Aguilar-Ruiz, Mario Piattini, Francisco J. Ferrer-Troyano, Francisco Ruiz:
A Comparison of Effort Estimation Methods for 4gl Programs: Experiences with Statistics and Data Mining. Int. J. Softw. Eng. Knowl. Eng. 16(1): 127-140 (2006) - [j16]Roberto Ruiz Sánchez, José Cristóbal Riquelme Santos, Jesús S. Aguilar-Ruiz:
Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognit. 39(12): 2383-2392 (2006) - [c61]Francisco J. Ferrer-Troyano, Jesús S. Aguilar-Ruiz, José Cristóbal Riquelme Santos:
Data streams classification by incremental rule learning with parameterized generalization. SAC 2006: 657-661 - [e2]José Cristóbal Riquelme Santos, Pere Botella:
XI Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2006), Octubre 3-6, 2006, Sitges, Barcelona, Spain. 2006, ISBN 84-95999-99-4 [contents] - 2005
- [j15]Francisco J. Ferrer-Troyano, Jesús S. Aguilar-Ruiz, José Cristóbal Riquelme Santos:
Incremental Rule Learning and Border Examples Selection from Numerical Data Streams. J. Univers. Comput. Sci. 11(8): 1426-1439 (2005) - [j14]Francisco J. Ferrer-Troyano, Jesús S. Aguilar-Ruiz, José Cristóbal Riquelme Santos:
Connecting Segments for Visual Data Exploration and Interactive Mining of Decision Rules. J. Univers. Comput. Sci. 11(11): 1835-1848 (2005) - [j13]Raúl Giráldez, Jesús S. Aguilar-Ruiz, José Cristóbal Riquelme Santos:
Knowledge-based fast evaluation for evolutionary learning. IEEE Trans. Syst. Man Cybern. Part C 35(2): 254-261 (2005) - [c60]