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Miguel García-Torres
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2020 – today
- 2024
- [j21]Miguel García-Torres
, Diego P. Pinto-Roa, Carlos Nunez Castillo, Brenda Quiñonez
Feature selection applied to QoS/QoE modeling on video and web-based mobile data services: An ordinal approach. Comput. Commun. 217: 230-245 (2024) - [j20]Gustavo Sosa-Cabrera
Feature selection: a perspective on inter-attribute cooperation. Int. J. Data Sci. Anal. 17(2): 139-151 (2024) - [j19]P. Britto Corthis, G. P. Ramesh
Effective Identification and Authentication of Healthcare IoT Using Fog Computing with Hybrid Cryptographic Algorithm. Symmetry 16(6): 726 (2024) - 2023
- [j18]Miguel García-Torres, Roberto Ruiz
Evolutionary feature selection on high dimensional data using a search space reduction approach. Eng. Appl. Artif. Intell. 117(Part): 105556 (2023) - [c21]José Luis Vázquez Noguera, Julio César Mello Román
Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement - A Neuroevolution Approach. CLEI 2023: 1-10 - [i2]Gustavo Sosa-Cabrera, Santiago Gómez-Guerrero, Miguel García-Torres, Christian E. Schaerer:
Feature Selection: A perspective on inter-attribute cooperation. CoRR abs/2306.16559 (2023) - 2022
- [j17]Santiago Gómez-Guerrero
Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty. Entropy 24(1): 64 (2022) - 2021
- [j16]Sebastián A. Grillo, Julio César Mello Román
Adjacent Inputs With Different Labels and Hardness in Supervised Learning. IEEE Access 9: 162487-162498 (2021) - [j15]Aurelio López-Fernández
A multi-GPU biclustering algorithm for binary datasets. J. Parallel Distributed Comput. 147: 209-219 (2021) - [j14]Jordan Ayala, Miguel García-Torres
Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowl. Based Syst. 225: 107119 (2021) - [j13]Pedro Manuel Martínez García
Genome-wide prediction of topoisomerase IIβ binding by architectural factors and chromatin accessibility. PLoS Comput. Biol. 17(1) (2021) - [c20]Miguel García-Torres, Francisco Gómez-Vela, Federico Divina, Diego P. Pinto-Roa, José Luis Vázquez Noguera, Julio César Mello Román
Scatter search for high-dimensional feature selection using feature grouping. GECCO Companion 2021: 149-150 - [c19]Diego Aquino-Brítez, Jordan Ayala Gómez, José Luis Vázquez Noguera, Miguel García-Torres, Julio César Mello Román
Automatic Diagnosis of Diabetic Retinopathy from Fundus Images Using Neuro-Evolutionary Algorithms. MedInfo 2021: 689-693 - [c18]Rodrigo Parra, Verena Ojeda, José Luis Vázquez Noguera, Miguel García-Torres, Julio César Mello Román
Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks. MIE 2021: 173-177 - [c17]José A. Gallardo, Miguel García-Torres, Francisco Gómez-Vela, Félix Morales, Federico Divina, David Becerra-Alonso, Gustavo Velázquez, Federico Daumas-Ladouce, José Luis Vázquez Noguera, Carlos Sauer Ayala, Diego P. Pinto-Roa, Pedro E. Gardel-Sotomayor, Julio César Mello Román
Forecasting Electricity Consumption Data from Paraguay Using a Machine Learning Approach. SOCO 2021: 685-694 - 2020
- [j12]Domingo S. Rodríguez-Baena
Identifying livestock behavior patterns based on accelerometer dataset. J. Comput. Sci. 41: 101076 (2020) - [c16]Federico Daumas-Ladouce
Multi-Objective Pareto Histogram Equalization. CLEI Selected Papers 2020: 3-23 - [c15]Viviana Elizabeth Jiménez Chaves
Analysis of Student Achievement Scores via Cluster Analysis. ICEUTE 2020: 399-408
2010 – 2019
- 2019
- [j11]Jorge Daniel Mello-Román
Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay. Comput. Math. Methods Medicine 2019: 7307803:1-7307803:7 (2019) - [j10]Julio César Mello Román
Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform. Entropy 21(3): 244 (2019) - [j9]Francisco Gómez-Vela
Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks. Entropy 21(12): 1139 (2019) - [j8]Gustavo Sosa-Cabrera, Miguel García-Torres
A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem. Inf. Sci. 494: 1-20 (2019) - [j7]Nelson Luis Durañona Sosa
RGB Inter-Channel Measures for Morphological Color Texture Characterization. Symmetry 11(10): 1190 (2019) - [c14]Brenda Quiñonez, Diego P. Pinto-Roa, Miguel García-Torres, María E. García-Díaz, Carlos Nunez Castillo, Federico Divina:
Map-Elites Algorithm for Features Selection Problem. AMW 2019 - [c13]Miguel García-Torres
Analysis of Student Achievement Scores: A Machine Learning Approach. CISIS-ICEUTE 2019: 275-284 - [c12]Viviana Elizabeth Jiménez Chaves
Analysis of Teacher Training in Mathematics in Paraguay's Elementary Education System Using Machine Learning Techniques. CISIS-ICEUTE 2019: 285-294 - [c11]Viviana Elizabeth Jiménez Chaves, Graciela Molinas Santana, Andrea Paola Riego Esteche
Self-Assessment of the Computer Engineering Career at the Universidad Americana. ICACIT 2019: 1-6 - 2018
- [j6]Federico Divina
Soft Computing for Analysis of Biomedical Data. Comput. Math. Methods Medicine 2018: 3902484:1-3902484:2 (2018) - [c10]Raul Mendez, Rodolfo Cardozo, José Luis Vázquez Noguera, Horacio Legal Ayala
Color Image Enhancement Using a Multiscale Morphological Approach. CACIC 2018: 109-123 - [c9]Pedro Manuel Martínez García
Analysis of Relevance and Redundance on Topoisomerase 2b (TOP2B) Binding Sites: A Feature Selection Approach. EvoApplications 2018: 86-101 - 2017
- [c8]Francisco Gómez-Vela
Bioinformatics from a Big Data Perspective: Meeting the Challenge. IWBBIO (2) 2017: 349-359 - [i1]Gustavo Sosa-Cabrera, Miguel García-Torres, Santiago Gómez, Christian Schaerer, Federico Divina:
Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection. CoRR abs/1709.08730 (2017) - 2016
- [j5]Miguel García-Torres
High-dimensional feature selection via feature grouping: A Variable Neighborhood Search approach. Inf. Sci. 326: 102-118 (2016) - [c7]Rafael Arias-Michel, Miguel García-Torres
Feature Selection Using Approximate Multivariate Markov Blankets. HAIS 2016: 114-125 - 2013
- [j4]Miguel García-Torres
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Inf. Sci. 222: 229-246 (2013) - 2012
- [j3]Roberto Ruiz
Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches. Expert Syst. Appl. 39(12): 11094-11102 (2012) - 2011
- [j2]Rubén Armañanzas
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 760-774 (2011) - [c6]Miguel García-Torres
A Two-Phase Heuristic Construction of Feature Sets for Classification. ICTAI 2011: 1028-1031 - 2010
- [c5]Miguel Á. Montero, Roberto Ruiz
Feature Selection Applied to Data from the Sloan Digital Sky Survey. IEA/AIE (1) 2010: 611-620
2000 – 2009
- 2009
- [r1]Miguel García-Torres:
Metaheuristics in Data Mining. Encyclopedia of Data Warehousing and Mining 2009: 1200-1206 - 2007
- [c4]Alejandro Hernández-Arauzo, Miguel García-Torres, Antonio Bahamonde:
Ranking Attributes Using Learning of Preferences by Means of SVM. CAEPIA 2007: 100-109 - 2006
- [j1]Félix García-López, Miguel García-Torres
Solving feature subset selection problem by a Parallel Scatter Search. Eur. J. Oper. Res. 169(2): 477-489 (2006) - 2005
- [c3]Ignacio José García del Amo, Miguel García-Torres, Belén Melián-Batista, José A. Moreno-Pérez, J. Marcos Moreno-Vega, Raquel Rivero Martín:
Data Mining with Scatter Search. EUROCAST 2005: 199-204 - 2004
- [c2]Miguel García-Torres, Félix C. García López, Belén Melián-Batista, José A. Moreno-Pérez, J. Marcos Moreno-Vega:
Solving Feature Subset Selection Problem by a Hybrid Metaheuristic. Hybrid Metaheuristics 2004: 59-68 - 2003
- [c1]Félix C. García López, Miguel García-Torres, José A. Moreno-Pérez, J. Marcos Moreno-Vega:
Scatter Search for the Feature Selection Problem. CAEPIA 2003: 517-525
Coauthor Index
aka: Francisco Antonio Gómez Vela
aka: José Luis Vázquez Noguera
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last updated on 2024-08-05 20:24 CEST by the dblp team
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