A Survey of Preprocessing Techniques for Flow Cytometry Data in Classification Tasks.

José A. Sáez José Antonio Sáez https://orcid.org/0000-0002-4592-1538

David Núñez-Nepomuceno José A. Sáez Pedro Carmona-Saez A Survey of Preprocessing Techniques for Flow Cytometry Data in Classification Tasks. 3112-3130 2025 22 IEEE Trans. Comput. Biol. Bioinform. 6 https://doi.org/10.1109/tcbbio.2025.3612573 db/journals/tcbb/tcbb22.html#NunezNepomucenoSC25 streams/journals/tcbb

Ismael Benito-Altamirano Omar Romera-Aller Miquel Alfaras Zouhair Haddi Anaïs Espinoso Xavier Llauradó José A. Sáez Cristian Fàbrega Gallego When Colorimetry Meets the Cloud: IoT-Enabled Health Monitoring at Home via Highly-Specific Gas Sensing. 1-11 2025 AINA (3) https://doi.org/10.1007/978-3-031-87769-8_1 conf/aina/2025-3 db/conf/aina/aina2025-3.html#Benito-Altamirano25

Juan Martín José A. Sáez Emilio Corchado Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction. 121608 2024 March 237 Expert Syst. Appl. Part B https://doi.org/10.1016/j.eswa.2023.121608 db/journals/eswa/eswa237.html#MartinSC24

José A. Sáez José Fernando Vera Compact Class-Conditional Attribute Category Clustering: Amino Acid Grouping for Enhanced HIV-1 Protease Cleavage Classification. 2167-2178 2024 November - December 21 IEEE ACM Trans. Comput. Biol. Bioinform. 6 https://doi.org/10.1109/TCBB.2024.3448617 db/journals/tcbb/tcbb21.html#SaezV24 streams/journals/tcbb

José A. Sáez Emilio Corchado ANCES: A novel method to repair attribute noise in classification problems. 108198 2022 121 Pattern Recognit. https://doi.org/10.1016/j.patcog.2021.108198 db/journals/pr/pr121.html#SaezC22

Juan Martín José A. Sáez Emilio Corchado On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters. 145800-145816 2021 9 IEEE Access https://doi.org/10.1109/ACCESS.2021.3123151 db/journals/access/access9.html#MartinSC21

Juan Martín José A. Sáez Emilio Corchado On the suitability of stacking-based ensembles in smart agriculture for evapotranspiration prediction. 107509 2021 108 Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2021.107509 https://www.wikidata.org/entity/Q120511999 db/journals/asc/asc108.html#MartinSC21

José A. Sáez Emilio Corchado A Meta-Learning Recommendation System for Characterizing Unsupervised Problems: On Using Quality Indices to Describe Data Conformations. 63247-63263 2019 7 IEEE Access https://doi.org/10.1109/ACCESS.2019.2917004 https://www.wikidata.org/entity/Q125006269 db/journals/access/access7.html#SaezC19

José A. Sáez Mikel Galar Bartosz Krawczyk Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy. 83396-83411 2019 7 IEEE Access https://doi.org/10.1109/ACCESS.2019.2925300 https://www.wikidata.org/entity/Q127617812 db/journals/access/access7.html#SaezGK19

José A. Sáez Emilio Corchado KSUFS: A Novel Unsupervised Feature Selection Method Based on Statistical Tests for Standard and Big Data Problems. 99754-99770 2019 7 IEEE Access https://doi.org/10.1109/ACCESS.2019.2930355 https://www.wikidata.org/entity/Q114629533 db/journals/access/access7.html#SaezC19a

José A. Sáez Pablo J. Villacorta Emilio Corchado Dataset Weighting via Intrinsic Data Characteristics for Pairwise Statistical Comparisons in Classification. 61-72 2019 HAIS https://doi.org/10.1007/978-3-030-29859-3_6 conf/hais/2019 db/conf/hais/hais2019.html#SaezVC19 Manuel Graña José Manuel López-Guede Oier Etxaniz Álvaro Herrero 0001 José Antonio Sáez Héctor Quintián Emilio Corchado International Joint Conference SOCO'18-CISIS'18-ICEUTE'18 - San Sebastián, Spain, June 6-8, 2018, Proceedings SOCO-CISIS-ICEUTE Springer 2019 Advances in Intelligent Systems and Computing 771 978-3-319-94119-6 978-3-319-94120-2 https://doi.org/10.1007/978-3-319-94120-2 https://www.wikidata.org/entity/Q54932146 db/conf/softcomp/soco2018.html José A. Sáez Héctor Quintián Bartosz Krawczyk Michal Wozniak 0001 Emilio Corchado Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. 131-142 2018 HAIS https://doi.org/10.1007/978-3-319-92639-1_12 https://www.wikidata.org/entity/Q62040573 conf/hais/2018 db/conf/hais/hais2018.html#SaezQKWC18 Francisco Javier de Cos Juez José Ramón Villar 0001 Enrique A. de la Cal Álvaro Herrero 0001 Héctor Quintián José Antonio Sáez Emilio Corchado Hybrid Artificial Intelligent Systems - 13th International Conference, HAIS 2018, Oviedo, Spain, June 20-22, 2018, Proceedings HAIS Springer 2018 Lecture Notes in Computer Science 10870 978-3-319-92638-4 978-3-319-92639-1 https://doi.org/10.1007/978-3-319-92639-1 https://www.wikidata.org/entity/Q113847367 db/conf/hais/hais2018.html

José A. Sáez Bartosz Krawczyk Michal Wozniak 0001 On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods. 590-609 2016 30 Appl. Artif. Intell. 6 https://doi.org/10.1080/08839514.2016.1193719 https://www.wikidata.org/entity/Q58280837 db/journals/aai/aai30.html#SaezKW16

José A. Sáez Julián Luengo Francisco Herrera Evaluating the classifier behavior with noisy data considering performance and robustness: The Equalized Loss of Accuracy measure. 26-35 2016 176 Neurocomputing https://doi.org/10.1016/j.neucom.2014.11.086 db/journals/ijon/ijon176.html#SaezLH16

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. 19-32 2016 27 Inf. Fusion https://doi.org/10.1016/j.inffus.2015.04.002 db/journals/inffus/inffus27.html#SaezGLH16

José A. Sáez Bartosz Krawczyk Michal Wozniak 0001 Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. 164-178 2016 57 Pattern Recognit. https://doi.org/10.1016/j.patcog.2016.03.012 https://www.wikidata.org/entity/Q62630334 db/journals/pr/pr57.html#SaezKW16

Bartosz Krawczyk José A. Sáez Michal Wozniak 0001 Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines. 915-922 2016 FUZZ-IEEE https://doi.org/10.1109/FUZZ-IEEE.2016.7737786 conf/fuzzIEEE/2016 db/conf/fuzzIEEE/fuzzIEEE2016.html#KrawczykSW16

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. 184-203 2015 291 Inf. Sci. https://doi.org/10.1016/j.ins.2014.08.051 db/journals/isci/isci291.html#SaezLSH15

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. 153-164 2015 90 Knowl. Based Syst. https://doi.org/10.1016/j.knosys.2015.09.023 https://www.wikidata.org/entity/Q57739401 db/journals/kbs/kbs90.html#GarciaSLLCH15

Pablo J. Villacorta José A. Sáez SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R. 89 2015 7 R J. 2 https://doi.org/10.32614/rj-2015-023 db/journals/rjour/rjour7.html#VillacortaS15

Isaac Triguero José A. Sáez Julián Luengo Salvador García 0001 Francisco Herrera On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. 30-41 2014 132 Neurocomputing https://doi.org/10.1016/j.neucom.2013.05.055 db/journals/ijon/ijon132.html#TrigueroSLGH14

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. 179-206 2014 38 Knowl. Inf. Syst. 1 https://doi.org/10.1007/s10115-012-0570-1 db/journals/kais/kais38.html#SaezGLH14

José A. Sáez Joaquín Derrac Julián Luengo Francisco Herrera Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. 3941-3948 2014 47 Pattern Recognit. 12 https://doi.org/10.1016/j.patcog.2014.06.012 db/journals/pr/pr47.html#SaezDLH14

José A. Sáez Joaquín Derrac Julián Luengo Francisco Herrera Improving the Behavior of the Nearest Neighbor Classifier against Noisy Data with Feature Weighting Schemes. 597-606 2014 HAIS https://doi.org/10.1007/978-3-319-07617-1_52 conf/hais/2014 db/conf/hais/hais2014.html#SaezDLH14 José A. Sáez Julián Luengo Jerzy Stefanowski Francisco Herrera Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering. 61-68 2014 IDEAL https://doi.org/10.1007/978-3-319-10840-7_8 conf/ideal/2014 db/conf/ideal/ideal2014.html#SaezLSH14

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. 1-20 2013 247 Inf. Sci. https://doi.org/10.1016/j.ins.2013.06.002 db/journals/isci/isci247.html#SaezGLH13

José A. Sáez Julián Luengo Francisco Herrera Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. 355-364 2013 46 Pattern Recognit. 1 https://doi.org/10.1016/j.patcog.2012.07.009 https://www.wikidata.org/entity/Q104699435 db/journals/pr/pr46.html#SaezLH13

Salvador García 0001 Julián Luengo José Antonio Sáez Victoria López Francisco Herrera A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. 734-750 2013 25 IEEE Trans. Knowl. Data Eng. 4 https://doi.org/10.1109/TKDE.2012.35 http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.35 https://www.wikidata.org/entity/Q57865032 db/journals/tkde/tkde25.html#GarciaLSLH13

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. 568-577 2013 HAIS https://doi.org/10.1007/978-3-642-40846-5_57 conf/hais/2013 db/conf/hais/hais2013.html#SaezGLH13

Julián Luengo José A. Sáez Francisco Herrera Missing data imputation for fuzzy rule-based classification systems. 863-881 2012 16 Soft Comput. 5 https://doi.org/10.1007/s00500-011-0774-4 https://www.wikidata.org/entity/Q57865402 db/journals/soco/soco16.html#LuengoSH12

Jose G. Moreno-Torres José A. Sáez Francisco Herrera Study on the Impact of Partition-Induced Dataset Shift on k -Fold Cross-Validation. 1304-1312 2012 23 IEEE Trans. Neural Networks Learn. Syst. 8 https://doi.org/10.1109/TNNLS.2012.2199516 https://www.wikidata.org/entity/Q57865546 db/journals/tnn/tnn23.html#Moreno-TorresSH12

José A. Sáez Mikel Galar Julián Luengo Francisco Herrera A First Study on Decomposition Strategies with Data with Class Noise Using Decision Trees. 25-35 2012 HAIS (2) https://doi.org/10.1007/978-3-642-28931-6_3 conf/hais/2012-2 db/conf/hais/hais2012-2.html#SaezGLH12 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. 1229-1234 2011 ISDA https://doi.org/10.1109/ISDA.2011.6121827 conf/isda/2011 db/conf/isda/isda2011.html#SaezLH11 José A. Sáez Julián Luengo Francisco Herrera A first study on the noise impact in classes for Fuzzy Rule Based Classification Systems. 153-158 2010 ISKE https://doi.org/10.1109/ISKE.2010.5680814 conf/iske/2010 db/conf/iske/iske2010.html#SaezLH10 Miquel Alfaras Ismael Benito-Altamirano Enrique A. de la Cal Pedro Carmona-Saez André C. P. L. F. de Carvalho Emilio Corchado Joaquín Derrac Anaïs Espinoso Oier Etxaniz Cristian FàbregaCristian Fàbrega Gallego Mikel Galar Luís Paulo F. Garcia Salvador García 0001 Manuel Graña Zouhair Haddi Francisco Herrera Álvaro Herrero 0001 Francisco Javier de Cos Juez Bartosz Krawczyk Xavier Llauradó Victoria López José Manuel López-Guede Ana Carolina Lorena Julián Luengo Juan Martín Jose G. Moreno-Torres David Núñez-Nepomuceno Héctor Quintián Omar Romera-Aller Jerzy Stefanowski Isaac Triguero José Fernando Vera Pablo J. Villacorta José R. Villar 0001José Ramón Villar 0001 Michal Wozniak 0001