Publications
2022
1.
Ferreras, Aitor Del Río; Fidalgo, Eduardo; Blanco-Medina, Pablo; Chaves, Deisy; Prieto-Castro, Alexci; Alegre, Enrique
Semantic Attention Keypoint Filtering for Darknet Content Classification Artículo de revista
En: VII Jornadas Nacionales de Investigación en Ciberseguridad 2022, 2022.
Resumen | Enlaces | BibTeX | Etiquetas: Actas, Ciberseguridad, Classification, Darknet Content, VII Jornadas Nacionales
@article{del_rio_ferreras_semantic_2022,
title = {Semantic Attention Keypoint Filtering for Darknet Content Classification},
author = {Aitor Del Río Ferreras and Eduardo Fidalgo and Pablo Blanco-Medina and Deisy Chaves and Alexci Prieto-Castro and Enrique Alegre},
url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9206652},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {VII Jornadas Nacionales de Investigación en Ciberseguridad 2022},
abstract = {Investigación en Ciberseguridad Actas de las VII Jornadas Nacionales (7º.2022.Bilbao)},
keywords = {Actas, Ciberseguridad, Classification, Darknet Content, VII Jornadas Nacionales},
pubstate = {published},
tppubtype = {article}
}
Investigación en Ciberseguridad Actas de las VII Jornadas Nacionales (7º.2022.Bilbao)
2019
2.
Panizo-Alonso, Luis
Learning process analysis using machine learning techniques Artículo de revista
En: 2019, (Publisher: OSF Preprints).
Resumen | Enlaces | BibTeX | Etiquetas: Classification, Constructivism, machine learning, Moodle
@article{panizo-alonso_learning_2019,
title = {Learning process analysis using machine learning techniques},
author = {Luis Panizo-Alonso},
url = {https://osf.io/preprints/osf/srhz9},
year = {2019},
date = {2019-01-01},
abstract = {This paper introduces a method for evaluating the learning-teaching process using machine learning techniques, specifically through data visualization and multidimensional scaling. It applies this method to eight diverse courses, offering insights into students' learning behaviors. The approach helps identify learning patterns, either confirming assumptions or revealing new insights. The results from 426 students highlight the usefulness of this technique in providing feedback to adjust teaching methods, exemplified by a case study where a course's methodology shifted to blended learning using Moodle.},
note = {Publisher: OSF Preprints},
keywords = {Classification, Constructivism, machine learning, Moodle},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a method for evaluating the learning-teaching process using machine learning techniques, specifically through data visualization and multidimensional scaling. It applies this method to eight diverse courses, offering insights into students' learning behaviors. The approach helps identify learning patterns, either confirming assumptions or revealing new insights. The results from 426 students highlight the usefulness of this technique in providing feedback to adjust teaching methods, exemplified by a case study where a course's methodology shifted to blended learning using Moodle.
2018
3.
Pellegrini, Enrico; Ballerini, Lucía; del Carmen Valdés-Hernández, María; Chappell, Francesca M; González-Castro, Victor; Anblagan, Devasuda; Danso, Samuel; Muñoz-Maniega, Susana; Job, Dominic; Pernet, Cyril
Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review Artículo de revista
En: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 10, pp. 519–535, 2018, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: Cerebrovascular Disease, Classification, Dementia, machine learning, MRI, Pathological Aging, segmentation, small vessel disease
@article{pellegrini_machine_2018,
title = {Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review},
author = {Enrico Pellegrini and Lucía Ballerini and María del Carmen Valdés-Hernández and Francesca M Chappell and Victor González-Castro and Devasuda Anblagan and Samuel Danso and Susana Muñoz-Maniega and Dominic Job and Cyril Pernet},
url = {https://www.sciencedirect.com/science/article/pii/S2352872918300447},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring},
volume = {10},
pages = {519–535},
abstract = {Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.},
note = {Publisher: No longer published by Elsevier},
keywords = {Cerebrovascular Disease, Classification, Dementia, machine learning, MRI, Pathological Aging, segmentation, small vessel disease},
pubstate = {published},
tppubtype = {article}
}
Advanced machine learning methods might help to identify dementia risk from neuroimaging, but their accuracy to date is unclear.
2007
4.
Alaiz-Rodríguez, Rocío; Guerrero-Curieses, Alicia; Cid-Sueiro, Jesús
Minimax regret classifier for imprecise class distributions Artículo de revista
En: Journal of Machine Learning Research, vol. 8, no 4, pp. 103–130, 2007.
Resumen | Enlaces | BibTeX | Etiquetas: Classification, Imprecise Class Distribution, Minimax Deviation, Minimax Regret, neural networks
@article{alaiz-rodriguez_minimax_2007,
title = {Minimax regret classifier for imprecise class distributions},
author = {Rocío Alaiz-Rodríguez and Alicia Guerrero-Curieses and Jesús Cid-Sueiro},
url = {https://www.jmlr.org/papers/volume8/alaiz-rodriguez07a/alaiz-rodriguez07a.pdf},
year = {2007},
date = {2007-01-01},
journal = {Journal of Machine Learning Research},
volume = {8},
number = {4},
pages = {103–130},
abstract = {This paper addresses the challenge of designing a classifier when the stationarity assumption—i.e., the agreement between training and test conditions—does not hold in real-world applications. In these cases, misclassification costs and data generation processes may differ between training and testing. The paper proposes a minimax regret (minimax deviation) approach to classifier design, aiming to minimize the maximum deviation from the optimal risk classifier's performance. Unlike traditional minimax methods, which can lead to severe performance degradation, this approach offers a more robust classification without significant loss of accuracy. The paper presents a neural-based minimax regret classifier for multi-class decision problems and demonstrates its robustness through experimental results.},
keywords = {Classification, Imprecise Class Distribution, Minimax Deviation, Minimax Regret, neural networks},
pubstate = {published},
tppubtype = {article}
}
This paper addresses the challenge of designing a classifier when the stationarity assumption—i.e., the agreement between training and test conditions—does not hold in real-world applications. In these cases, misclassification costs and data generation processes may differ between training and testing. The paper proposes a minimax regret (minimax deviation) approach to classifier design, aiming to minimize the maximum deviation from the optimal risk classifier's performance. Unlike traditional minimax methods, which can lead to severe performance degradation, this approach offers a more robust classification without significant loss of accuracy. The paper presents a neural-based minimax regret classifier for multi-class decision problems and demonstrates its robustness through experimental results.
2006
5.
Sánchez-González, Lidia; Petkov, Nicolai; Alegre, Enrique
Statistical approach to boar semen evaluation using intracellular intensity distribution of head images Artículo de revista
En: Cellular and molecular biology, vol. 52, no 6, pp. 38–43, 2006.
Resumen | Enlaces | BibTeX | Etiquetas: boar semen, Classification, Concentration of Alive Cells, image analysis, Intracellular Intensity Distribution, Morphometry, SPERM, Sperm Heads
@article{sanchez-gonzalez_statistical_2006,
title = {Statistical approach to boar semen evaluation using intracellular intensity distribution of head images},
author = {Lidia Sánchez-González and Nicolai Petkov and Enrique Alegre},
url = {https://research.rug.nl/en/publications/statistical-approach-to-boar-semen-evaluation-using-intracellular},
year = {2006},
date = {2006-01-01},
urldate = {2006-01-01},
journal = {Cellular and molecular biology},
volume = {52},
number = {6},
pages = {38–43},
abstract = {This study presents a method to classify boar sperm heads by analyzing intracellular intensity distributions in microscopic images. After pre-processing the images, a model of intensity distribution for living cells is created. Deviations from this model are used to classify sperm as alive or dead. The method provides accurate estimations of live cell fractions, with an error margin of less than 8%, meeting veterinary requirements.},
keywords = {boar semen, Classification, Concentration of Alive Cells, image analysis, Intracellular Intensity Distribution, Morphometry, SPERM, Sperm Heads},
pubstate = {published},
tppubtype = {article}
}
This study presents a method to classify boar sperm heads by analyzing intracellular intensity distributions in microscopic images. After pre-processing the images, a model of intensity distribution for living cells is created. Deviations from this model are used to classify sperm as alive or dead. The method provides accurate estimations of live cell fractions, with an error margin of less than 8%, meeting veterinary requirements.
0000
6.
Moreno-Torres, José G; Raeder, Troy; Alaiz-Rodríguez, Rocío; Chawla, Nitesh V; Herrera, Francisco
Tackling dataset shift in classification: Benchmarks and methods Miscelánea
0000.
Resumen | Enlaces | BibTeX | Etiquetas: Algorithm Comparison, Benchmark Dataset, Classification, dataset shift, Transfer Learning
@misc{moreno-torres_tackling_nodate,
title = {Tackling dataset shift in classification: Benchmarks and methods},
author = {José G Moreno-Torres and Troy Raeder and Rocío Alaiz-Rodríguez and Nitesh V Chawla and Francisco Herrera},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=2gj1UNYAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=2gj1UNYAAAAJ:0EnyYjriUFMC},
abstract = {This paper addresses the issue of dataset shift, which occurs when the data used to train a classifier differs from the data distribution it encounters during deployment. This phenomenon can lead to poor performance of the classifier, similar to the impact of noisy data. The paper introduces a new benchmark set of datasets to facilitate fair comparisons of algorithms designed to handle dataset shift. The study also includes a comprehensive analysis of key algorithms in the field, evaluating their effectiveness across a range of datasets and shifts.},
keywords = {Algorithm Comparison, Benchmark Dataset, Classification, dataset shift, Transfer Learning},
pubstate = {published},
tppubtype = {misc}
}
This paper addresses the issue of dataset shift, which occurs when the data used to train a classifier differs from the data distribution it encounters during deployment. This phenomenon can lead to poor performance of the classifier, similar to the impact of noisy data. The paper introduces a new benchmark set of datasets to facilitate fair comparisons of algorithms designed to handle dataset shift. The study also includes a comprehensive analysis of key algorithms in the field, evaluating their effectiveness across a range of datasets and shifts.
7.
Barreiro, Joaquín; Sanz, Alberto; Hernández, LK; Alegre, Enrique; Castejón-Limas, Manuel
Operator and analyst interfaces for monitoring of wear in tool inserts Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Classification, Computer vision, descriptors, monitoring tool life
@article{barreiro_operator_nodate,
title = {Operator and analyst interfaces for monitoring of wear in tool inserts},
author = {Joaquín Barreiro and Alberto Sanz and LK Hernández and Enrique Alegre and Manuel Castejón-Limas},
url = {https://d1wqtxts1xzle7.cloudfront.net/47455745/Operator_and_analyst_interfaces_for_moni20160723-29317-10pmzkk-libre.pdf?1469286941=&response-content-disposition=inline%3B+filename%3DOperator_and_analyst_interfaces_for_moni.pdf&Expires=1739527854&Signature=RWcL6iwivos9e0wdZGv2op4N17pTROrpur~Yrv~yUJp2ta5PuCsKYNk-gnEyq2hojbariQ5iVTq7oPhutXCpZHCk5x~oE5Z~mH5mTrzcuxUCRwc4hRZlUY~HlJbpBLtJDTKagp2VLdb2UYRaqWMREe4VwJnMKyW8DjlgQtY4NiMx8ECM6FWcANQUhPZmRvT2RLYKEYbI9NMLGaW4qs9FTGATkhPSmkFJ6nt6QJQ4VkGmn55XlN3gE7hYbUDGTr6oZ7LzpBlvKWq856FUqSPD2Uy6Wz0~oQ8NyZM9ynDMGOSEyu640BiwmmSWfvHOuEv9mIkoFy95Kyx9lrf73kSs5A__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
abstract = {Computer vision techniques have advanced significantly, enabling their effective application in industrial environments. This paper presents graphical interfaces for operators and analysts to monitor tool inserts during steel turning. The system integrates computer vision with classification techniques based on statistical moments and region descriptors, enhancing inspection and control processes.},
keywords = {Classification, Computer vision, descriptors, monitoring tool life},
pubstate = {published},
tppubtype = {article}
}
Computer vision techniques have advanced significantly, enabling their effective application in industrial environments. This paper presents graphical interfaces for operators and analysts to monitor tool inserts during steel turning. The system integrates computer vision with classification techniques based on statistical moments and region descriptors, enhancing inspection and control processes.