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2020 – today
- 2024
- [j20]Nicolás García-Pedrajas
, José Manuel Cuevas-Muñoz
Evolutionary simultaneous under and oversampling of instances for dealing with class-imbalance datasets in multilabel problems. Appl. Soft Comput. 159: 111618 (2024) - [j19]Nicolás García-Pedrajas
Extensive experimental comparison among multilabel methods focused on ranking performance. Inf. Sci. 679: 121074 (2024) - [j18]Nicolás García-Pedrajas
A thorough experimental comparison of multilabel methods for classification performance. Pattern Recognit. 151: 110342 (2024) - 2023
- [j17]Nicolás García-Pedrajas
PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification. Inf. Fusion 95: 120-142 (2023) - 2022
- [j16]Aida de Haro-García
Grab'Em: A Novel Graph-Based Method for Combining Feature Subset Selectors. IEEE Trans. Cybern. 52(5): 2942-2954 (2022) - 2021
- [j15]Javier Pérez-Rodríguez
Floating Search Methodology for Combining Classification Models for Site Recognition in DNA Sequences. IEEE ACM Trans. Comput. Biol. Bioinform. 18(6): 2471-2482 (2021) - 2020
- [j14]Aida de Haro-García, Gonzalo Cerruela García
Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study. Inf. Sci. 540: 89-116 (2020) - [j13]Gonzalo Cerruela García
Influence of feature rankers in the construction of molecular activity prediction models. J. Comput. Aided Mol. Des. 34(3): 305-325 (2020)
2010 – 2019
- 2019
- [j12]Gonzalo Cerruela García
Improving the combination of results in the ensembles of prototype selectors. Neural Networks 118: 175-191 (2019) - [j11]Aida de Haro-García
Instance selection based on boosting for instance-based learners. Pattern Recognit. 96 (2019) - 2018
- [j10]Javier Pérez-Rodríguez
A general framework for boosting feature subset selection algorithms. Inf. Fusion 44: 147-175 (2018) - [j9]Aida de Haro-García
Combining three strategies for evolutionary instance selection for instance-based learning. Swarm Evol. Comput. 42: 160-172 (2018) - 2014
- [j8]Nicolás García-Pedrajas, Aida de Haro-García
A Scalable Memetic Algorithm for Simultaneous Instance and Feature Selection. Evol. Comput. 22(1): 1-45 (2014) - [j7]Nicolás García-Pedrajas, Aida de Haro-García
Boosting instance selection algorithms. Knowl. Based Syst. 67: 342-360 (2014) - 2013
- [j6]Nicolás García-Pedrajas, Aida de Haro-García
A scalable approach to simultaneous evolutionary instance and feature selection. Inf. Sci. 228: 150-174 (2013) - [j5]Nicolás García-Pedrajas, Javier Pérez-Rodríguez
OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets. IEEE Trans. Cybern. 43(1): 332-346 (2013) - 2012
- [j4]Aida de Haro-García
Large scale instance selection by means of federal instance selection. Data Knowl. Eng. 75: 58-77 (2012) - [j3]Nicolás García-Pedrajas, Aida de Haro-García
Scaling up data mining algorithms: review and taxonomy. Prog. Artif. Intell. 1(1): 71-87 (2012) - [p1]Aida de Haro-García, Javier Pérez-Rodríguez, Nicolás García-Pedrajas:
A Scalable Feature Selection Method to Improve the Analysis of Microarrays. Modern Advances in Intelligent Systems and Tools 2012: 87-92 - 2011
- [c6]Aida de Haro-García
A Comparison of Two Strategies for Scaling Up Instance Selection in Huge Datasets. CAEPIA 2011: 64-73 - [c5]Javier Pérez-Rodríguez
Instance Selection for Class Imbalanced Problems by Means of Selecting Instances More than Once. CAEPIA 2011: 104-113 - [c4]Aida de Haro-García
Feature Selection for Translation Initiation Site Recognition. IEA/AIE (2) 2011: 357-366 - [c3]Aida de Haro-García
A scalable method for instance selection for class-imbalance datasets. ISDA 2011: 1383-1390 - 2010
- [j2]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) - [c2]Aida de Haro-García
Large Scale Instance Selection by Means of a Parallel Algorithm. IDEAL 2010: 1-12 - [c1]Aida de Haro-García
Scaling Up Feature Selection by Means of Democratization. IEA/AIE (2) 2010: 662-672
2000 – 2009
- 2009
- [j1]Aida de Haro-García
A divide-and-conquer recursive approach for scaling up instance selection algorithms. Data Min. Knowl. Discov. 18(3): 392-418 (2009)
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