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María Martínez-Ballesteros
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2020 – today
- 2024
- [j20]M. J. Jiménez-Navarro
, María Martínez-Ballesteros
Explaining deep learning models for ozone pollution prediction via embedded feature selection. Appl. Soft Comput. 157: 111504 (2024) - [c22]Maria Lourdes Linares-Barrera
Multi-Objective Lagged Feature Selection Based on Dependence Coefficient for Time-Series Forecasting. CAEPIA 2024: 81-90 - [c21]Maria Lourdes Linares-Barrera
Evolutionary Feature Selection for Time-Series Forecasting. SAC 2024: 395-399 - 2023
- [j19]A. R. Troncoso-García, María Martínez-Ballesteros
A new approach based on association rules to add explainability to time series forecasting models. Inf. Fusion 94: 169-180 (2023) - [j18]M. J. Jiménez-Navarro
PHILNet: A novel efficient approach for time series forecasting using deep learning. Inf. Sci. 632: 815-832 (2023) - [j17]Manuel Jesús Jiménez-Navarro
A new deep learning architecture with inductive bias balance for transformer oil temperature forecasting. J. Big Data 10(1): 80 (2023) - [c20]Manuel Jesús Jiménez-Navarro
Association Rule Analysis of Student Satisfaction Surveys for Teaching Quality Evaluation. CISIS-ICEUTE 2023: 319-328 - [c19]E. Tefera, María Martínez-Ballesteros
A New Hybrid CNN-LSTM for Wind Power Forecasting in Ethiopia. HAIS 2023: 207-218 - [c18]Maria Lourdes Linares-Barrera
A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma. HAIS 2023: 731-742 - [c17]M. J. Jiménez-Navarro
Embedded Temporal Feature Selection for Time Series Forecasting Using Deep Learning. IWANN (2) 2023: 15-26 - [c16]A. R. Troncoso-García, María Martínez-Ballesteros
Deep Learning-Based Approach for Sleep Apnea Detection Using Physiological Signals. IWANN (1) 2023: 626-637 - [c15]Angela Troncoso-García
Evolutionary computation to explain deep learning models for time series forecasting. SAC 2023: 433-436 - [c14]Manuel Jesús Jiménez-Navarro
A bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal. SAC 2023: 441-448 - [c13]M. J. Jiménez-Navarro
Explaining Learned Patterns in Deep Learning by Association Rules Mining. SOCO (2) 2023: 132-141 - 2022
- [c12]A. R. Troncoso-García, María Martínez-Ballesteros
Explainable machine learning for sleep apnea prediction. KES 2022: 2930-2939 - [c11]C. Segarra-Martín, María Martínez-Ballesteros
A novel approach to discover numerical association based on the coronavirus optimization algorithm. SAC 2022: 1148-1151 - [c10]Manuel Jesús Jiménez-Navarro
Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. SOCO 2022: 557-566 - 2021
- [j16]Rubén Martín Payo
Spanish adaptation and validation of the User Version of the Mobile Application Rating Scale (uMARS). J. Am. Medical Informatics Assoc. 28(12): 2681-2686 (2021) - 2020
- [j15]Laura Macías-García
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance. Artif. Intell. Medicine 110: 101976 (2020)
2010 – 2019
- 2019
- [j14]José María Luna-Romera
Analysis of the Evolution of the Spanish Labour Market Through Unsupervised Learning. IEEE Access 7: 121695-121708 (2019) - [j13]José María Luna-Romera
External clustering validity index based on chi-squared statistical test. Inf. Sci. 487: 1-17 (2019) - 2018
- [j12]Diana Martín, María Martínez-Ballesteros
MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems. Knowl. Based Syst. 153: 176-192 (2018) - [j11]José María Luna-Romera
An approach to validity indices for clustering techniques in Big Data. Prog. Artif. Intell. 7(2): 81-94 (2018) - 2017
- [j10]Francisco Martínez-Álvarez
Applications of Computational Intelligence in Time Series. Comput. Intell. Neurosci. 2017: 9361749:1-9361749:2 (2017) - [j9]María Martínez-Ballesteros
Machine learning techniques to discover genes with potential prognosis role in Alzheimer's disease using different biological sources. Inf. Fusion 36: 114-129 (2017) - [j8]Laura Macías-García
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation. J. Biomed. Informatics 72: 33-44 (2017) - 2016
- [j7]María Martínez-Ballesteros
Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176: 36-47 (2016) - [j6]María Martínez-Ballesteros
Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowl. Inf. Syst. 49(2): 481-509 (2016) - [c9]José María Luna-Romera
An Approach to Silhouette and Dunn Clustering Indices Applied to Big Data in Spark. CAEPIA 2016: 160-169 - [c8]Ricardo L. Talavera-Llames
A Nearest Neighbours-Based Algorithm for Big Time Series Data Forecasting. HAIS 2016: 174-185 - [c7]Alejandro Sánchez Medina, Alberto Gil Pichardo, José Manuel García-Heredia
Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. HAIS 2016: 694-705 - 2015
- [j5]María Martínez-Ballesteros
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) - 2014
- [j4]María Martínez-Ballesteros
Selecting the best measures to discover quantitative association rules. Neurocomputing 126: 3-14 (2014) - [j3]María Martínez-Ballesteros
Discovering gene association networks by multi-objective evolutionary quantitative association rules. J. Comput. Syst. Sci. 80(1): 118-136 (2014) - 2013
- [c6]María Martínez-Ballesteros
ra A Sensitivity Analysis for Quality Measures of Quantitative Association Rules. HAIS 2013: 578-587 - 2011
- [j2]María Martínez-Ballesteros
An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Comput. 15(10): 2065-2084 (2011) - [c5]María Martínez-Ballesteros
Analysis of Measures of Quantitative Association Rules. HAIS (2) 2011: 319-326 - [c4]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 - [c3]Cristina Rubio-Escudero, Francisco Martínez-Álvarez
On the use of algorithms to discover motifs in DNA sequences. ISDA 2011: 1074-1079 - [c2]María Martínez-Ballesteros
Inferring gene-gene associations from Quantitative Association Rules. ISDA 2011: 1241-1246 - 2010
- [j1]María Martínez-Ballesteros
Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integr. Comput. Aided Eng. 17(3): 227-242 (2010)
2000 – 2009
- 2009
- [c1]María Martínez-Ballesteros
Quantitative Association Rules Applied to Climatological Time Series Forecasting. IDEAL 2009: 284-291
Coauthor Index
aka: Manuel Jesús Jiménez-Navarro
aka: Alicia Troncoso
aka: José C. Riquelme
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last updated on 2024-10-07 21:14 CEST by the dblp team
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