Julián Luengo

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 55

Top Topics

Constrained Clustering

Unsupervised Anomaly Detection

Top Venues

Publications

Iago Xabier Vázquez, Bahgat Ayasi, Huseyin Seker, Julián Luengo, Javier Sedano, Ángel Miguel García-Vico
Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasites. Appl. Soft Comput. 160 (2024)
Iván Sevillano-García, Julián Luengo, Francisco Herrera
SHIELD: A regularization technique for eXplainable Artificial Intelligence. CoRR (2024)
David López, Ignacio Aguilera-Martos, Marta García-Bárzana, Francisco Herrera, Diego García-Gil, Julián Luengo
Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series. Inf. Fusion 100 (2023)
Ignacio Aguilera-Martos, Marta García-Bárzana, Diego García-Gil, Jacinto Carrasco, David López, Julián Luengo, Francisco Herrera
Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study. Neurocomputing 544 (2023)
Iván Sevillano-García, Julián Luengo, Francisco Herrera
Low-Impact Feature Reduction Regularization Term: How to Improve Artificial Intelligence with Explainability. xAI (Late-breaking Work, Demos, Doctoral Consortium) (2023)
Iván Sevillano-García, Julián Luengo, Francisco Herrera
REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study. Int. J. Intell. Syst. 2023 (2023)
Iván Sevillano-García, Julián Luengo, Francisco Herrera
Optimizing LIME Explanations Using REVEL Metrics. HAIS (2023)
Adrián Peláez-Vegas, Pablo Mesejo, Julián Luengo
A Survey on Semi-Supervised Semantic Segmentation. CoRR (2023)
Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera
TSFEDL: A python library for time series spatio-temporal feature extraction and prediction using deep learning. Neurocomputing 517 (2023)
Ignacio Aguilera-Martos, Julián Luengo, Francisco Herrera
Revisiting Histogram Based Outlier Scores: Strengths and Weaknesses. HAIS (2023)
Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García
3SHACC: Three stages hybrid agglomerative constrained clustering. Neurocomputing 490 (2022)
Miriam Seoane Santos, Pedro Henriques Abreu, Alberto Fernández, Julián Luengo, João A. M. Santos
The impact of heterogeneous distance functions on missing data imputation and classification performance. Eng. Appl. Artif. Intell. 111 (2022)
Ignacio Aguilera-Martos, Ángel Miguel García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study). CoRR (2022)
Julián Luengo, Raúl Moreno, Iván Sevillano-García, David Charte, Adrián Peláez-Vegas, Marta Fernández-Moreno, Pablo Mesejo, Francisco Herrera
A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. Inf. Fusion 78 (2022)
Manuel González, Julián Luengo, José Ramón Cano, Salvador García
Synthetic Sample Generation for Label Distribution Learning. Inf. Sci. 544 (2021)
Julián Luengo, Dánel Sánchez Tarragó, Ronaldo C. Prati, Francisco Herrera
Multiple instance classification: Bag noise filtering for negative instance noise cleaning. Inf. Sci. 579 (2021)
Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García
ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm Evol. Comput. 66 (2021)
Jacinto Carrasco, Irina Markova, David López, Ignacio Aguilera, Diego García, Marta García-Bárzana, Manuel Arias-Rodil, Julián Luengo, Francisco Herrera
Anomaly Detection in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms. CoRR (2021)
Jacinto Carrasco, David López, Ignacio Aguilera-Martos, Diego García-Gil, Irina Markova, Marta García-Bárzana, Manuel Arias-Rodil, Julián Luengo, Francisco Herrera
Anomaly detection in predictive maintenance: A new evaluation framework for temporal unsupervised anomaly detection algorithms. Neurocomputing 462 (2021)
Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García
Enhancing instance-level constrained clustering through differential evolution. Appl. Soft Comput. 108 (2021)
Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training. CoRR (2021)
Germán González-Almagro, Julián Luengo, José Ramón Cano, Salvador García
DILS: Constrained clustering through dual iterative local search. Comput. Oper. Res. 121 (2020)
Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, Francisco Herrera
COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images. CoRR (2020)
Jesús Maillo, Salvador García, Julián Luengo, Francisco Herrera, Isaac Triguero
Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data. IEEE Trans. Fuzzy Syst. 28 (5) (2020)
Germán González-Almagro, Juan-Luis Suárez, Julián Luengo, José Ramón Cano, Salvador García
Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. HAIS (2020)
Germán González-Almagro, Alejandro Rosales-Pérez, Julián Luengo, José Ramón Cano, Salvador García
Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO (2020)
Juan Antonio Cortés-Ibáñez, Sergio González, José Javier Valle-Alonso, Julián Luengo, Salvador García, Francisco Herrera
Preprocessing methodology for time series: An industrial world application case study. Inf. Sci. 514 (2020)
José Ramón Cano, Julián Luengo, Salvador García
Similarity-based and Iterative Label Noise Filters for Monotonic Classification. HICSS (2020)
Siham Tabik, Anabel Gómez-Ríos, J. L. Martín-Rodríguez, I. Sevillano-García, Manuel Rey-Area, David Charte, Emilio Guirado, Juan-Luis Suárez, Julián Luengo, M. A. Valero-González, P. García-Villanova, E. Olmedo-Sánchez, Francisco Herrera
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE J. Biomed. Health Informatics 24 (12) (2020)
Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera
Enabling Smart Data: Noise filtering in Big Data classification. Inf. Sci. 479 (2019)
Diego García-Gil, Alejandro Alcalde-Barros, Julián Luengo, Salvador García, Francisco Herrera
Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. IoTBDS (2019)
Besay Montesdeoca, Julián Luengo, Jesús Maillo, Diego García-Gil, Salvador García, Francisco Herrera
A First Approach on Big Data Missing Values Imputation. IoTBDS (2019)
Isaac Triguero, Diego García-Gil, Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera
Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data. WIREs Data Mining Knowl. Discov. 9 (2) (2019)
Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Francisco Herrera
Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks. Knowl. Based Syst. 184 (2019)
Diego García-Gil, Francisco Luque Sánchez, Julián Luengo, Salvador García, Francisco Herrera
From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. Int. J. Intell. Syst. 34 (12) (2019)
Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera
Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst. Appl. 118 (2019)
Ronaldo C. Prati, Julián Luengo, Francisco Herrera
Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise. Knowl. Inf. Syst. 60 (1) (2019)
Ignacio Cordón, Julián Luengo, Salvador García, Francisco Herrera, Francisco Charte
: Data preprocessing to achieve smart data in R. Neurocomputing 360 (2019)
José Ramón Cano, Julián Luengo, Salvador García
Label noise filtering techniques to improve monotonic classification. Neurocomputing 353 (2019)
Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation. CoRR (2018)
Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera, Isaac Triguero
A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. FUZZ-IEEE (2018)
José Ramón Cano, Julián Luengo, Salvador García
Label Noise Filtering Techniques to Improve Monotonic Classification. CoRR (2018)
Julián Luengo, Seong-O Shim, Saleh Alshomrani, Abdulrahman H. Altalhi, Francisco Herrera
CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring. Knowl. Based Syst. 140 (2018)
Julián Luengo, Dánel Sánchez Tarragó, Ronaldo C. Prati, Francisco Herrera
A First Study on the Use of Noise Filtering to Clean the Bags in Multi-Instance Classification. LOPAL (2018)
Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera
Enabling Smart Data: Noise filtering in Big Data classification. CoRR (2017)
Isaac Triguero, Sergio González, Jose M. Moyano, Salvador García, Jesús Alcalá-Fdez, Julián Luengo, Alberto Fernández, María José del Jesus, Luciano Sánchez, Francisco Herrera
KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. Int. J. Comput. Intell. Syst. 10 (1) (2017)
Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera
A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost. HAIS (2017)
Pablo Morales, Julián Luengo, Luís Paulo F. Garcia, Ana Carolina Lorena, André C. P. L. F. de Carvalho, Francisco Herrera
The NoiseFiltersR Package: Label Noise Preprocessing in R. R J. 9 (1) (2017)
Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera, Isaac Triguero
Exact fuzzy k-nearest neighbor classification for big datasets. FUZZ-IEEE (2017)
José A. Sáez, Julián Luengo, Francisco Herrera
Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. Neurocomputing 176 (2016)
José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera
INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inf. Fusion 27 (2016)
Salvador García, Julián Luengo, Francisco Herrera
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl. Based Syst. 98 (2016)
Julián Luengo, Ángel Miguel García-Vico, M. Dolores Pérez-Godoy, Cristóbal J. Carmona
The influence of noise on the evolutionary fuzzy systems for subgroup discovery. Soft Comput. 20 (11) (2016)
Isaac Triguero, Jesús Maillo, Julián Luengo, Salvador García, Francisco Herrera
From Big Data to Smart Data with the K-Nearest Neighbours Algorithm. iThings/GreenCom/CPSCom/SmartData (2016)
Pablo Morales-Alvarez, Julián Luengo, Francisco Herrera
A First Study on the Use of Boosting for Class Noise Reparation. HAIS (2016)
Julián Luengo, Francisco Herrera
An automatic extraction method of the domains of competence for learning classifiers using data complexity measures. Knowl. Inf. Syst. 42 (1) (2015)
José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera
SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf. Sci. 291 (2015)
Luís Paulo F. Garcia, José A. Sáez, Julián Luengo, Ana Carolina Lorena, André C. P. L. F. de Carvalho, Francisco Herrera
Using the One-vs-One decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowl. Based Syst. 90 (2015)
Julián Luengo, Rafael Rumí
Naive Bayes Classifier with Mixtures of Polynomials. ICPRAM (1) (2015)
Cristóbal J. Carmona, Julián Luengo
A First Approach in the Class Noise Filtering Approaches for Fuzzy Subgroup Discovery. SOCO (2015)
José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera
Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. Pattern Recognit. 47 (12) (2014)
José A. Sáez, Julián Luengo, Jerzy Stefanowski, Francisco Herrera
Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. IDEAL (2014)
José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera
Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. Knowl. Inf. Syst. 38 (1) (2014)
Isaac Triguero, José A. Sáez, Julián Luengo, Salvador García, Francisco Herrera
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing 132 (2014)
José A. Sáez, Joaquín Derrac, Julián Luengo, Francisco Herrera
Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes. HAIS (2014)
José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera
An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets. HAIS (2013)
José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera
Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness. Inf. Sci. 247 (2013)
José A. Sáez, Julián Luengo, Francisco Herrera
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit. 46 (1) (2013)
Salvador García, Julián Luengo, José Antonio Sáez, Victoria López, Francisco Herrera
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. IEEE Trans. Knowl. Data Eng. 25 (4) (2013)
Cristóbal J. Carmona, Julián Luengo, Pedro González, María José del Jesus
An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery. Expert Syst. Appl. 39 (13) (2012)
Julián Luengo, Salvador García, Francisco Herrera
On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowl. Inf. Syst. 32 (1) (2012)
Salvador García, Victoria López, Julián Luengo, Cristóbal J. Carmona, Francisco Herrera
A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. ICPRAM (1) (2012)
José A. Sáez, Mikel Galar, Julián Luengo, Francisco Herrera
A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. HAIS (2) (2012)
Julián Luengo, José A. Sáez, Francisco Herrera
Missing data imputation for fuzzy rule-based classification systems. Soft Comput. 16 (5) (2012)
Cristóbal J. Carmona, Julián Luengo, Pedro González, María José del Jesus
A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery. FUZZ-IEEE (2012)
Julián Luengo, Francisco Herrera
Shared domains of competence of approximate learning models using measures of separability of classes. Inf. Sci. 185 (1) (2012)
Julián Luengo, Alberto Fernández, Salvador García, Francisco Herrera
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling. Soft Comput. 15 (10) (2011)
Jesús Alcalá-Fdez, Alberto Fernández, Julián Luengo, Joaquín Derrac, Salvador García
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. J. Multiple Valued Log. Soft Comput. 17 (2-3) (2011)
José A. Sáez, Julián Luengo, Francisco Herrera
Fuzzy Rule Based Classification Systems versus crisp robust learners trained in presence of class noise's effects: A case of study. ISDA (2011)
Salvador García, Joaquín Derrac, Julián Luengo, Cristóbal J. Carmona, Francisco Herrera
Evolutionary selection of hyperrectangles in nested generalized exemplar learning. Appl. Soft Comput. 11 (3) (2011)
Alberto Fernández, Salvador García, Julián Luengo, Ester Bernadó-Mansilla, Francisco Herrera
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study. IEEE Trans. Evol. Comput. 14 (6) (2010)
Julián Luengo, Francisco Herrera
Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method. Fuzzy Sets Syst. 161 (1) (2010)
Salvador García, Alberto Fernández, Julián Luengo, Francisco Herrera
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180 (10) (2010)
José A. Sáez, Julián Luengo, Francisco Herrera
A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. ISKE (2010)
Julián Luengo, Salvador García, Francisco Herrera
A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method. Neural Networks 23 (3) (2010)
Julián Luengo, Francisco Herrera
An extraction method for the characterization of the Fuzzy Rule Based Classification Systems' behavior using data complexity measures: A case of study with FH-GBML. FUZZ-IEEE (2010)
Julián Luengo, Francisco Herrera
Domains of Competence of Artificial Neural Networks Using Measures of Separability of Classes. IWANN (1) (2009)
Salvador García, Alberto Fernández, Julián Luengo, Francisco Herrera
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13 (10) (2009)
Alberto Fernández, Julián Luengo, Joaquín Derrac, Jesús Alcalá-Fdez, Francisco Herrera
Implementation and Integration of Algorithms into the KEEL Data-Mining Software Tool. IDEAL (2009)
Salvador García, Joaquín Derrac, Julián Luengo, Francisco Herrera
A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms. ISDA (2009)
Julián Luengo, Salvador García, Francisco Herrera
A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests. Expert Syst. Appl. 36 (4) (2009)
Julián Luengo, Francisco Herrera
On the use of Measures of Separability of Classes to Characterise the Domains of Competence of a Fuzzy Rule Based Classification System. IFSA/EUSFLAT Conf. (2009)
Julián Luengo, Alberto Fernández, Salvador García, Francisco Herrera
Addressing Data-Complexity for Imbalanced Data-Sets: A Preliminary Study on the Use of Preprocessing for C4.5. ISDA (2009)
Julián Luengo, Salvador García, Francisco Herrera
A Study on the Use of Statistical Tests for Experimentation with Neural Networks. IWANN (2007)