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César Ignacio García-Osorio
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- affiliation: University of Burgos, Spain
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2020 – today
- 2024
- [j33]José Antonio Barbero-Aparicio
, Alicia Olivares-Gil, Juan José Rodríguez
Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques. Inf. Fusion 102: 102035 (2024) - [j32]José Luis Garrido-Labrador
Ensemble methods and semi-supervised learning for information fusion: A review and future research directions. Inf. Fusion 107: 102310 (2024) - [c20]José Manuel Aroca, José-Francisco Díez-Pastor, Pedro Latorre-Carmona, Antonio Canepa-Oneto, Juan Carlos Rad, Gustau Camps-Valls, Víctor Elvira, César García-Osorio:
WALGREEN: Web Based Platform for Soil Organic Carbon Inference Applications. IGARSS 2024: 3992-3996 - 2023
- [j31]José Antonio Barbero-Aparicio
Deep learning and support vector machines for transcription start site identification. PeerJ Comput. Sci. 9: e1340 (2023) - 2022
- [j30]José Antonio Barbero-Aparicio
Nonlinear physics opens a new paradigm for accurate transcription start site prediction. BMC Bioinform. 23(1): 565 (2022) - [j29]Ismael Ramos-Pérez
When is resampling beneficial for feature selection with imbalanced wide data? Expert Syst. Appl. 188: 116015 (2022) - [c19]Antonio Salguero-Cruz, Pedro Latorre-Carmona, César Ignacio García-Osorio
Proposal of a Comparative Framework for Face Super-Resolution Algorithms in Forensics. IbPRIA 2022: 458-469 - [i1]Francisco Javier Yagüe, José-Francisco Díez-Pastor, Pedro Latorre-Carmona, César Ignacio García-Osorio:
Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework. CoRR abs/2202.13945 (2022) - 2021
- [j28]Mario Juez-Gil, Álvar Arnaiz-González
Experimental evaluation of ensemble classifiers for imbalance in Big Data. Appl. Soft Comput. 108: 107447 (2021) - [j27]Mario Juez-Gil, Álvar Arnaiz-González, Juan José Rodríguez
Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark. Neurocomputing 464: 432-437 (2021) - [j26]Mario Juez-Gil
Rotation Forest for Big Data. Inf. Fusion 74: 39-49 (2021)
2010 – 2019
- 2019
- [j25]Miroslaw Kordos
Evolutionary prototype selection for multi-output regression. Neurocomputing 358: 309-320 (2019) - 2018
- [j24]Álvar Arnaiz-González
Local sets for multi-label instance selection. Appl. Soft Comput. 68: 651-666 (2018) - [j23]Álvar Arnaiz-González
Seshat - a web-based educational resource for teaching the most common algorithms of lexical analysis. Comput. Appl. Eng. Educ. 26(6): 2255-2265 (2018) - [j22]Álvar Arnaiz-González
Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning. Expert Syst. Appl. 109: 114-130 (2018) - 2016
- [j21]Álvar Arnaiz-González
Instance selection for regression by discretization. Expert Syst. Appl. 54: 340-350 (2016) - [j20]Álvar Arnaiz-González
Instance selection for regression: Adapting DROP. Neurocomputing 201: 66-81 (2016) - [j19]Álvar Arnaiz-González
Fusion of instance selection methods in regression tasks. Inf. Fusion 30: 69-79 (2016) - [j18]Álvar Arnaiz-González
Instance selection of linear complexity for big data. Knowl. Based Syst. 107: 83-95 (2016) - [j17]Álvar Arnaiz-González
Random feature weights for regression trees. Prog. Artif. Intell. 5(2): 91-103 (2016) - 2015
- [j16]José-Francisco Díez-Pastor, Juan José Rodríguez
Diversity techniques improve the performance of the best imbalance learning ensembles. Inf. Sci. 325: 98-117 (2015) - [j15]José-Francisco Díez-Pastor, Juan José Rodríguez Diez
Random Balance: Ensembles of variable priors classifiers for imbalanced data. Knowl. Based Syst. 85: 96-111 (2015) - [c18]Juan José Rodríguez Diez
An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees. MCS 2015: 181-193 - 2014
- [j14]José-Francisco Díez-Pastor, César Ignacio García-Osorio
Tree ensemble construction using a GRASP-based heuristic and annealed randomness. Inf. Fusion 20: 189-202 (2014) - 2013
- [j13]Carlos Pardo
Rotation Forests for regression. Appl. Math. Comput. 219(19): 9914-9924 (2013) - [j12]Nicolás García-Pedrajas, César Ignacio García-Osorio
Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections. Prog. Artif. Intell. 2(1): 29-44 (2013) - [c17]José-Francisco Díez-Pastor, César Ignacio García-Osorio
Imbalanced Learning Ensembles for Defect Detection in X-Ray Images. IEA/AIE 2013: 654-663 - [c16]Juan José Rodríguez Diez
Random Oracle Ensembles for Imbalanced Data. MCS 2013: 247-258 - 2012
- [j11]Jesús Maudes
Random feature weights for decision tree ensemble construction. Inf. Fusion 13(1): 20-30 (2012) - [j10]Nicolás García-Pedrajas, Jesús Manuel Maudes Raedo
Supervised subspace projections for constructing ensembles of classifiers. Inf. Sci. 193: 1-21 (2012) - [j9]José-Francisco Díez-Pastor, Andrés Bustillo
Boosting Projections to improve surface roughness prediction in high-torque milling operations. Soft Comput. 16(8): 1427-1437 (2012) - [c15]Juan José Rodríguez Diez
Disturbing Neighbors Ensembles of Trees for Imbalanced Data. ICMLA (2) 2012: 83-88 - [c14]Carlos Pardo-Aguilar, José-Francisco Díez-Pastor, Nicolás García-Pedrajas, Juan José Rodríguez Diez, César Ignacio García-Osorio:
Linear Projection Methods - An Experimental Study for Regression Problems. ICPRAM (1) 2012: 198-204 - 2011
- [j8]Jesús Maudes
Random projections for linear SVM ensembles. Appl. Intell. 34(3): 347-359 (2011) - [j7]Nicolás García-Pedrajas, César Ignacio García-Osorio
Constructing ensembles of classifiers using supervised projection methods based on misclassified instances. Expert Syst. Appl. 38(1): 343-359 (2011) - [c13]Juan J. Rodríguez Diez
Using Model Trees and Their Ensembles for Imbalanced Data. CAEPIA 2011: 94-103 - [c12]José-Francisco Díez-Pastor, César Ignacio García-Osorio
GRASP Forest: A New Ensemble Method for Trees. MCS 2011: 66-75 - [c11]Juan José Rodríguez
Ensembles of Decision Trees for Imbalanced Data. MCS 2011: 76-85 - [p2]Carlos Pardo
Random Oracles for Regression Ensembles. Ensembles in Machine Learning Applications 2011: 181-199 - 2010
- [j6]César Ignacio García-Osorio
Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts. Artif. Intell. 174(5-6): 410-441 (2010) - [j5]Juan José Rodríguez
Forests of nested dichotomies. Pattern Recognit. Lett. 31(2): 125-132 (2010) - [c10]Jesús Maudes
Random Projections for SVM Ensembles. IEA/AIE (2) 2010: 87-95 - [c9]Carlos Pardo
An Empirical Study of Multilayer Perceptron Ensembles for Regression Tasks. IEA/AIE (2) 2010: 106-115 - [c8]Juan José Rodríguez, César Ignacio García-Osorio
An Experimental Study on Ensembles of Functional Trees. MCS 2010: 64-73 - [e1]Colin Fyfe, Peter Tiño, Darryl Charles, César Ignacio García-Osorio, Hujun Yin:
Intelligent Data Engineering and Automated Learning - IDEAL 2010, 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Lecture Notes in Computer Science 6283, Springer 2010, ISBN 978-3-642-15380-8 [contents]
2000 – 2009
- 2009
- [c7]Jesús Maudes
Disturbing Neighbors Ensembles for Linear SVM. MCS 2009: 191-200 - [p1]Jesús Maudes
Disturbing Neighbors Diversity for Decision Forests. Applications of Supervised and Unsupervised Ensemble Methods 2009: 113-133 - 2008
- [c6]César Ignacio García-Osorio, Nicolás García-Pedrajas:
Constructing ensembles of classifiers using linear projections based on misclassified instances. ESANN 2008: 283-288 - [c5]César Ignacio García-Osorio, José-Francisco Díez-Pastor, Juan José Rodríguez, Jesús Maudes:
License Plate Number Recognition - New Heuristics and a Comparative Study of Classifiers. ICINCO-RA (1) 2008: 268-273 - [c4]César Ignacio García-Osorio
Teaching push-down automata and turing machines. ITiCSE 2008: 316 - [c3]César Ignacio García-Osorio
A tool for teaching LL and LR parsing algorithms. ITiCSE 2008: 317 - 2007
- [j4]Nicolás García-Pedrajas, César Ignacio García-Osorio, Colin Fyfe:
Nonlinear Boosting Projections for Ensemble Construction. J. Mach. Learn. Res. 8: 1-33 (2007) - [c2]Jesús Maudes, Juan José Rodríguez, César Ignacio García-Osorio
Cascading for Nominal Data. MCS 2007: 231-240 - 2005
- [b1]César García-Osorio:
Data mining and visualization. University of Exeter, UK, 2005 - [j3]César Ignacio García-Osorio
The combined use of self-organizing maps and andrews' curves. Int. J. Neural Syst. 15(3): 197-206 (2005) - [j2]César Ignacio García-Osorio
Regaining sparsity in kernel principal components. Neurocomputing 67: 398-402 (2005) - [j1]César Ignacio García-Osorio, Colin Fyfe:
Visualization of High-dimensional Data via Orthogonal Curves. J. Univers. Comput. Sci. 11(11): 1806-1819 (2005) - 2004
- [c1]César Ignacio García-Osorio, Jesús Maudes, Colin Fyfe:
Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps. ESANN 2004: 477-482
Coauthor Index
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