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Dr. Maria Perez-Ortiz
Artificial Intelligence Center, University College London, London WC1E 6BT, UK

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0 Image Processing
0 Machine Learning
0 Recommender Systems
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Preprint content
Published: 26 May 2021
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How do we best constrain social interactions to prevent the transmission of communicable respiratory diseases? Indiscriminate suppression, the currently accepted answer, is both unsustainable long term and implausibly presupposes all interactions to carry equal weight. Transmission within a social network is determined by the topology of its graphical structure, of which the number of interactions is only one aspect. Here we deploy large-scale numerical simulations to quantify the impact on pathogen transmission of a set of topological features covering the parameter space of realistic possibility. We first test through a series of stochastic simulations the differences in the spread of disease on several classes of network geometry (including highly skewed networks and small world). We then aim to characterise the spread based on the characteristics of the network topology using regression analysis, highlighting some of the network metrics that influence the spread the most. For this, we build a dataset composed of more than 9000 social networks and 30 topological network metrics. We find that pathogen spread is optimally reduced by limiting specific kinds of social contact – unfamiliar and long range – rather than their global number. Our results compel a revaluation of social interventions in communicable diseases, and the optimal approach to crafting them.

ACS Style

María Pérez-Ortiz; Petru Manescu; Fabio Caccioli; Delmiro Fernández-Reyes; Parashkev Nachev; John Shawe-Taylor. Disentangling network topology and pathogen spread. 2021, 1 .

AMA Style

María Pérez-Ortiz, Petru Manescu, Fabio Caccioli, Delmiro Fernández-Reyes, Parashkev Nachev, John Shawe-Taylor. Disentangling network topology and pathogen spread. . 2021; ():1.

Chicago/Turabian Style

María Pérez-Ortiz; Petru Manescu; Fabio Caccioli; Delmiro Fernández-Reyes; Parashkev Nachev; John Shawe-Taylor. 2021. "Disentangling network topology and pathogen spread." , no. : 1.

Preprint content
Published: 15 May 2021
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Background The success of social distancing implementations of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) depends heavily on population compliance. Mathematical modelling has been used extensively to assess the rate of viral transmission from behavioural responses. Previous epidemics of SARS-Cov-2 have been characterised by superspreaders, a small number of individuals who transmit a disease to a large group of individuals, who contribute to the stochasticity (or randomness) of transmission compared to other pathogens such as Influenza. This growing evidence proves an urgent matter to understand transmission routes in order to target and combat outbreaks. Objective To investigate the role of superspreaders in the rate of viral transmission with various levels of compliance. Method A SEIRS inspired social network model is adapted and calibrated to observe the infected links of a general population with and without superspreaders on four compliance levels. Local and global connection parameters are adjusted to simulate close contact networks and travel restrictions respectively and each performance assessed. The mean and standard deviation of infections with superspreaders and non-superspreaders were calculated for each compliance level. Results Increased levels of compliance of superspreaders proves a significant reduction in infections. Assuming long-lasting immunity, superspreaders could potentially slow down the spread due to their high connectivity. Discussion The main advantage of applying the network model is to capture the heterogeneity and locality of social networks, including the role of superspreaders in epidemic dynamics. The main challenge is the immediate attention on social settings with targeted interventions to tackle superspreaders in future empirical work. Conclusion Superspreaders play a central role in slowing down infection spread following compliance guidelines. It is crucial to adjust social distancing measures to prevent future outbreaks accompanied by population-wide testing and effective tracing.

ACS Style

Faith Lee; Maria Perez Ortiz; John Shawe-Taylor. Computational modelling of COVID-19: A study of compliance and superspreaders. 2021, 1 .

AMA Style

Faith Lee, Maria Perez Ortiz, John Shawe-Taylor. Computational modelling of COVID-19: A study of compliance and superspreaders. . 2021; ():1.

Chicago/Turabian Style

Faith Lee; Maria Perez Ortiz; John Shawe-Taylor. 2021. "Computational modelling of COVID-19: A study of compliance and superspreaders." , no. : 1.

Preprint content
Published: 02 February 2021
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Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical models at longer lead times and calibrating their forecasts can be challenging. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations covering 1850-2100 and observational data from 1979-2011 to forecast the next 6 months of monthly-averaged sea ice concentration maps. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice. It also demonstrates a greater ability to predict anomalous pan-Arctic sea ice extents than the models submitted to the Sea Ice Outlook programme. In addition, IceNet’s well-calibrated probabilistic forecasts mean it can reliably bound the ice edge between two contours. IceNet’s accuracy and reliability represent a step-change in sea ice forecasting, providing a robust framework to build early-warning systems and conservation tools that mitigate risks associated with rapid sea ice loss.

ACS Style

Tom R. Andersson; J. Scott Hosking; Maria Pérez-Ortiz; Brooks Paige; Andrew Elliott; Chris Russell; Stephen Law; Daniel C. Jones; Jeremy Wilkinson; Tony Phillips; Steffen Tietsche; Beena Sarojini; Eduardo Blanchard-Wrigglesworth; Yevgeny Aksenov; Rod Downie; Emily Shuckburgh. Seasonal Arctic sea ice forecasting with probabilistic deep learning. 2021, 1 .

AMA Style

Tom R. Andersson, J. Scott Hosking, Maria Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C. Jones, Jeremy Wilkinson, Tony Phillips, Steffen Tietsche, Beena Sarojini, Eduardo Blanchard-Wrigglesworth, Yevgeny Aksenov, Rod Downie, Emily Shuckburgh. Seasonal Arctic sea ice forecasting with probabilistic deep learning. . 2021; ():1.

Chicago/Turabian Style

Tom R. Andersson; J. Scott Hosking; Maria Pérez-Ortiz; Brooks Paige; Andrew Elliott; Chris Russell; Stephen Law; Daniel C. Jones; Jeremy Wilkinson; Tony Phillips; Steffen Tietsche; Beena Sarojini; Eduardo Blanchard-Wrigglesworth; Yevgeny Aksenov; Rod Downie; Emily Shuckburgh. 2021. "Seasonal Arctic sea ice forecasting with probabilistic deep learning." , no. : 1.

Conference paper
Published: 20 January 2020 in Proceedings of the 13th International Conference on Web Search and Data Mining
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ACS Style

Sahan Bulathwela; María Pérez-Ortiz; Rishabh Mehrotra; Davor Orlic; Colin De La Higuera; John Shawe-Taylor; Emine Yilmaz. SUM'20: State-based User Modelling. Proceedings of the 13th International Conference on Web Search and Data Mining 2020, 1 .

AMA Style

Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin De La Higuera, John Shawe-Taylor, Emine Yilmaz. SUM'20: State-based User Modelling. Proceedings of the 13th International Conference on Web Search and Data Mining. 2020; ():1.

Chicago/Turabian Style

Sahan Bulathwela; María Pérez-Ortiz; Rishabh Mehrotra; Davor Orlic; Colin De La Higuera; John Shawe-Taylor; Emine Yilmaz. 2020. "SUM'20: State-based User Modelling." Proceedings of the 13th International Conference on Web Search and Data Mining , no. : 1.

Conference paper
Published: 01 November 2019 in 2019 Picture Coding Symposium (PCS)
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Encoding images in a visually lossless manner helps to achieve the best trade-off between image compression performance and quality and so that compression artifacts are invisible to the majority of users. Visually lossless encoding can often be achieved by manually adjusting compression quality parameters of existing lossy compression methods, such as JPEG or WebP. But the required compression quality parameter can also be determined automatically using visibility metrics. However, creating an accurate visibility metric is challenging because of the complexity of the human visual system and the effort needed to collect the required data. In this paper, we investigate how to train an accurate visibility metric for visually lossless compression from a relatively small dataset. Our experiments show that prediction error can be reduced by 40% compared with the state-of-theart, and that our proposed method can save between 25%-75% of storage space compared with the default quality parameter used in commercial software. We demonstrate how the visibility metric can be used for visually lossless image compression and for benchmarking image compression encoders.

ACS Style

Nanyang Ye; María Pérez-Ortiz; Rafal K. Mantiuk. Visibility Metric for Visually Lossless Image Compression. 2019 Picture Coding Symposium (PCS) 2019, 1 -5.

AMA Style

Nanyang Ye, María Pérez-Ortiz, Rafal K. Mantiuk. Visibility Metric for Visually Lossless Image Compression. 2019 Picture Coding Symposium (PCS). 2019; ():1-5.

Chicago/Turabian Style

Nanyang Ye; María Pérez-Ortiz; Rafal K. Mantiuk. 2019. "Visibility Metric for Visually Lossless Image Compression." 2019 Picture Coding Symposium (PCS) , no. : 1-5.

Journal article
Published: 01 January 2019 in Neurocomputing
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ACS Style

M. Pérez-Ortiz; A.M. Durán-Rosal; Pedro Antonio Gutiérrez; Javier Sanchez-Monedero; Athanasia Nikolaou; Francisco Fernández-Navarro; C. Hervás-Martínez. On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing 2019, 326-327, 3 -14.

AMA Style

M. Pérez-Ortiz, A.M. Durán-Rosal, Pedro Antonio Gutiérrez, Javier Sanchez-Monedero, Athanasia Nikolaou, Francisco Fernández-Navarro, C. Hervás-Martínez. On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing. 2019; 326-327 ():3-14.

Chicago/Turabian Style

M. Pérez-Ortiz; A.M. Durán-Rosal; Pedro Antonio Gutiérrez; Javier Sanchez-Monedero; Athanasia Nikolaou; Francisco Fernández-Navarro; C. Hervás-Martínez. 2019. "On the use of evolutionary time series analysis for segmenting paleoclimate data." Neurocomputing 326-327, no. : 3-14.

Conference paper
Published: 01 October 2018 in 2018 25th IEEE International Conference on Image Processing (ICIP)
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In this paper, we propose a trained perceptually transform for quality assessment of high dynamic range (HDR) images and video. The transform is used to convert absolute luminance values found in HDR images into perceptually uniform units, which can be used with any standard-dynamic-range metric. The new transform is derived by fitting the parameters of a previously proposed perceptual encoding function to 4 different HDR subjective quality assessment datasets using Bayesian optimization. The new transform combined with a simple peak signal-to-noise ratio measure achieves better prediction performance in cross-dataset validation than existing transforms. We provide Matlab code for our metric 1 1https://github.com/ynyCL/T-PT-metric.

ACS Style

Nanyang Ye; María Pérez-Ortiz; Rafal K. Mantiuk. Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video. 2018 25th IEEE International Conference on Image Processing (ICIP) 2018, 1718 -1722.

AMA Style

Nanyang Ye, María Pérez-Ortiz, Rafal K. Mantiuk. Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video. 2018 25th IEEE International Conference on Image Processing (ICIP). 2018; ():1718-1722.

Chicago/Turabian Style

Nanyang Ye; María Pérez-Ortiz; Rafal K. Mantiuk. 2018. "Trained Perceptual Transform for Quality Assessment of High Dynamic Range Images and Video." 2018 25th IEEE International Conference on Image Processing (ICIP) , no. : 1718-1722.

Conference paper
Published: 01 July 2018 in 2018 International Joint Conference on Neural Networks (IJCNN)
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Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable.

ACS Style

María Pérez-Ortiz; Pedro Antonio Gutiérrez; Peter Tino; C. Casanova-Mateo; S. Salcedo-Sanz. A mixture of experts model for predicting persistent weather patterns. 2018 International Joint Conference on Neural Networks (IJCNN) 2018, 1 -8.

AMA Style

María Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tino, C. Casanova-Mateo, S. Salcedo-Sanz. A mixture of experts model for predicting persistent weather patterns. 2018 International Joint Conference on Neural Networks (IJCNN). 2018; ():1-8.

Chicago/Turabian Style

María Pérez-Ortiz; Pedro Antonio Gutiérrez; Peter Tino; C. Casanova-Mateo; S. Salcedo-Sanz. 2018. "A mixture of experts model for predicting persistent weather patterns." 2018 International Joint Conference on Neural Networks (IJCNN) , no. : 1-8.

Conference paper
Published: 01 May 2018 in 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)
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TID2013 is a subjective image quality assessment dataset with a wide range of distortion types and over 3000 images. The dataset has proven to be a challenging test for objective quality metrics. The dataset mean opinion scores were obtained by collecting pairwise comparison judgments using the Swiss tournament system, and averaging votes of observers. However, this approach differs from the usual analysis of multiple pairwise comparisons, which involves psychometric scaling of the comparison data using either Thurstone or Bradley-Terry models. In this paper we investigate how quality scores change when they are computed using such psychometric scaling instead of averaging vote counts. In order to properly scale TID2013 quality scores, we conduct four additional experiments of two different types, which we found necessary to produce a common quality scale: comparisons with reference images, and cross-content comparisons. We demonstrate on a fifth validation experiment that the two additional types of comparisons are necessary and in conjunction with psychometric scaling improve the consistency of quality scores, especially across images depicting different contents.

ACS Style

Aliaksei Mikhailiuk; María Pérez-Ortiz; Rafal Mantiuk. Psychometric scaling of TID2013 dataset. 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) 2018, 1 -6.

AMA Style

Aliaksei Mikhailiuk, María Pérez-Ortiz, Rafal Mantiuk. Psychometric scaling of TID2013 dataset. 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX). 2018; ():1-6.

Chicago/Turabian Style

Aliaksei Mikhailiuk; María Pérez-Ortiz; Rafal Mantiuk. 2018. "Psychometric scaling of TID2013 dataset." 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) , no. : 1-6.

Journal article
Published: 01 March 2018 in Applied Soft Computing
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ACS Style

Javier Sanchez-Monedero; María Pérez-Ortiz; Aurora Sáez; Pedro Antonio Gutiérrez; César Hervás-Martínez. Partial order label decomposition approaches for melanoma diagnosis. Applied Soft Computing 2018, 64, 341 -355.

AMA Style

Javier Sanchez-Monedero, María Pérez-Ortiz, Aurora Sáez, Pedro Antonio Gutiérrez, César Hervás-Martínez. Partial order label decomposition approaches for melanoma diagnosis. Applied Soft Computing. 2018; 64 ():341-355.

Chicago/Turabian Style

Javier Sanchez-Monedero; María Pérez-Ortiz; Aurora Sáez; Pedro Antonio Gutiérrez; César Hervás-Martínez. 2018. "Partial order label decomposition approaches for melanoma diagnosis." Applied Soft Computing 64, no. : 341-355.

Original article
Published: 16 September 2017 in Liver Transplantation
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In 2014, we reported a model for donor‐recipient (D‐R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR‐E]). The aim is to test the ANN‐based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King's College Hospital (KCH; n = 822). The ANN was trained and tested using KCH pairs for both 3‐ and 12‐month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule‐based system for facilitating the decision about the most appropriate D‐R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR–area under the curve [AUC] = 0.94; MS‐AUC = 0.94) and 12 months (CCR‐AUC = 0.78; MS‐AUC = 0.82), almost 15% higher than the best obtained by other known scores such as Model for End‐Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR‐E database. In conclusion, the use of ANN for D‐R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists. Liver Transplantation 24 192–203 2018 AASLD.

ACS Style

María Dolores Ayllón; Rubén Ciria; Manuel Cruz-Ramírez; María Pérez-Ortiz; Irene Gómez; Roberto Valente; John O'grady; Manuel De La Mata; César Hervás-Martínez; Nigel D. Heaton; Javier Briceño. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transplantation 2017, 24, 192 -203.

AMA Style

María Dolores Ayllón, Rubén Ciria, Manuel Cruz-Ramírez, María Pérez-Ortiz, Irene Gómez, Roberto Valente, John O'grady, Manuel De La Mata, César Hervás-Martínez, Nigel D. Heaton, Javier Briceño. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transplantation. 2017; 24 (2):192-203.

Chicago/Turabian Style

María Dolores Ayllón; Rubén Ciria; Manuel Cruz-Ramírez; María Pérez-Ortiz; Irene Gómez; Roberto Valente; John O'grady; Manuel De La Mata; César Hervás-Martínez; Nigel D. Heaton; Javier Briceño. 2017. "Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation." Liver Transplantation 24, no. 2: 192-203.

Conference paper
Published: 18 May 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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ACS Style

María Pérez-Ortiz; Kelwin Fernandes; Ricardo Cruz; Jaime Cardoso; Javier Briceño; César Hervás-Martínez. Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 525 -537.

AMA Style

María Pérez-Ortiz, Kelwin Fernandes, Ricardo Cruz, Jaime Cardoso, Javier Briceño, César Hervás-Martínez. Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():525-537.

Chicago/Turabian Style

María Pérez-Ortiz; Kelwin Fernandes; Ricardo Cruz; Jaime Cardoso; Javier Briceño; César Hervás-Martínez. 2017. "Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 525-537.

Conference paper
Published: 18 May 2017 in Computer Vision
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ACS Style

Pedro Antonio Gutiérrez; María Pérez-Ortiz; Alberto Suárez. Class Switching Ensembles for Ordinal Regression. Computer Vision 2017, 408 -419.

AMA Style

Pedro Antonio Gutiérrez, María Pérez-Ortiz, Alberto Suárez. Class Switching Ensembles for Ordinal Regression. Computer Vision. 2017; ():408-419.

Chicago/Turabian Style

Pedro Antonio Gutiérrez; María Pérez-Ortiz; Alberto Suárez. 2017. "Class Switching Ensembles for Ordinal Regression." Computer Vision , no. : 408-419.

Conference paper
Published: 18 May 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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ACS Style

Francisco Javier Maestre-García; Carlos García-Martínez; María Pérez-Ortiz; Pedro Antonio Gutiérrez. An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 513 -524.

AMA Style

Francisco Javier Maestre-García, Carlos García-Martínez, María Pérez-Ortiz, Pedro Antonio Gutiérrez. An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():513-524.

Chicago/Turabian Style

Francisco Javier Maestre-García; Carlos García-Martínez; María Pérez-Ortiz; Pedro Antonio Gutiérrez. 2017. "An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 513-524.

Journal article
Published: 01 May 2017 in Knowledge-Based Systems
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Liver transplantation is a promising and widely-accepted treatment for patients with terminal liver disease. However, transplantation is restricted by the lack of suitable donors, resulting in significant waiting list deaths. This paper proposes a novel donor-recipient allocation system that uses machine learning to predict graft survival after transplantation using a dataset comprised of donor-recipient pairs from the Kings College Hospital (United Kingdom). The main novelty of the system is that it tackles the imbalanced nature of the dataset by considering semi-supervised learning, analysing its potential for obtaining more robust and equitable models in liver transplantation. We propose two different sources of unsupervised data for this specific problem (recent transplants and virtual donor-recipient pairs) and two methods for using these data during model construction (a semi-supervised algorithm and a label propagation scheme). The virtual pairs and the label propagation method are shown to alleviate the imbalanced distribution. The results of our experiments show that the use of synthetic and real unsupervised information helps to improve and stabilise the performance of the model and leads to fairer decisions with respect to the use of only supervised data. Moreover, the best model is combined with the Model for End-stage Liver Disease score (MELD), which is at the moment the most popular assignation methodology worldwide. By doing this, our decision-support system considers both the compatibility of the donor and the recipient (by our prediction system) and the recipient severity (via the MELD score), supporting then the principles of fairness and benefit.

ACS Style

M. Pérez-Ortiz; Pedro Antonio Gutiérrez; M.D. Ayllón-Terán; N. Heaton; Ruben Ciria; J. Briceño; C. Hervás-Martínez. Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. Knowledge-Based Systems 2017, 123, 75 -87.

AMA Style

M. Pérez-Ortiz, Pedro Antonio Gutiérrez, M.D. Ayllón-Terán, N. Heaton, Ruben Ciria, J. Briceño, C. Hervás-Martínez. Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. Knowledge-Based Systems. 2017; 123 ():75-87.

Chicago/Turabian Style

M. Pérez-Ortiz; Pedro Antonio Gutiérrez; M.D. Ayllón-Terán; N. Heaton; Ruben Ciria; J. Briceño; C. Hervás-Martínez. 2017. "Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation." Knowledge-Based Systems 123, no. : 75-87.

Journal article
Published: 17 February 2017 in Artificial Intelligence in Medicine
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HighlightsThe problem of constructing an organ allocation decision-support system combining donor, recipiend and surgery characteristics using artificial neural networks is assessed.A dynamically weighted evolutionary algorithm alleviates the imbalanced nature of the dataset.Ordinal over-sampling techniques help balancing the training set and improve the results obtained by the classifiers.An ordinal artificial neural network has been proven to perform well for the 4-category classification problem assessed.An extended supranational experimental design for liver transplantation allocation could be feasible considering more countries. ObjectiveCreate an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of organ survival at different thresholds for each donorrecipient pair considered. Currently, this decision is mainly based on the Model for End-stage Liver Disease, which depends only on the severity of the recipient and obviates donorrecipient compatibility. We therefore propose to use information concerning the donor, the recipient and the surgery, with the objective of allocating the organ correctly. Methods and materialsThe database consists of information concerning transplants conducted in 7 different Spanish hospitals and the King's College Hospital (United Kingdom). The state of the patients is followed up for 12 months. We propose to treat the problem as an ordinal classification one, where we predict the organ survival at different thresholds: less than 15 days, between 15 and 90 days, between 90 and 365 days and more than 365 days. This discretization is intended to produce finer-grain survival information (compared with the common binary approach). However, it results in a highly imbalanced dataset in which more than 85% of cases belong to the last class. To solve this, we combine two approaches, a cost-sensitive evolutionary ordinal artificial neural network (ANN) (in which we propose to incorporate dynamic weights to make more emphasis on the worst classified classes) and an ordinal over-sampling technique (which adds virtual patterns to the minority classes and thus alleviates the imbalanced nature of the dataset). ResultsThe results obtained by our proposal are promising and satisfactory, considering the overall accuracy, the ordering of the classes and the sensitivity of minority classes. In this sense, both the dynamic costs and the over-sampling technique improve the base results of the considered ANN-based method. Comparing our model with other state-of-the-art techniques in ordinal classification, competitive results can also be appreciated. The results achieved with this proposal improve the ones obtained by other state-of-the-art models: we were able to correctly predict more than 73% of the transplantation results, with a geometric mean of the sensitivities of 31.46%, which is much higher than the one obtained by other models. ConclusionsThe combination of the proposed cost-sensitive evolutionary algorithm together with the application of an over-sampling technique improves the predictive capability of our model in a significant way (especially for minority classes), which can help the surgeons make more informed decisions about the most appropriate recipient for an specific donor organ, in order to maximize the probability of survival after the transplantation and therefore the fairness principle.

ACS Style

Manuel Dorado-Moreno; María Pérez-Ortiz; Pedro A. Gutiérrez; Rubén Ciria; Javier Brice No; César Hervás-Martínez. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artificial Intelligence in Medicine 2017, 77, 1 -11.

AMA Style

Manuel Dorado-Moreno, María Pérez-Ortiz, Pedro A. Gutiérrez, Rubén Ciria, Javier Brice No, César Hervás-Martínez. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artificial Intelligence in Medicine. 2017; 77 ():1-11.

Chicago/Turabian Style

Manuel Dorado-Moreno; María Pérez-Ortiz; Pedro A. Gutiérrez; Rubén Ciria; Javier Brice No; César Hervás-Martínez. 2017. "Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem." Artificial Intelligence in Medicine 77, no. : 1-11.

Proceedings article
Published: 01 December 2016 in 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
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The recently coined term “learning from label proportions” refers to a new learning paradigm where training data is given by groups (also denoted as “bags”), and the only known information is the label proportion of each bag. The aim is then to construct a classification model to predict the class label of an individual instance, which differentiates this paradigm from the one of multi-instance learning. This learning setting presents very different applications in political science, marketing, healthcare and, in general, all fields in relation with anonymous data. In this paper, two new strategies are proposed to tackle this kind of problems. Both proposals are based on the optimisation of pattern class memberships using the data distribution in each bag and the known label proportions. To do so, linear discriminant analysis has been reformulated to work with non-crisp class memberships. The experimental part of this paper sets different objetives: 1) study the difference in performance, comparing our proposals and the fully supervised setting, 2) analyse the potential benefits of refining class memberships by the proposed approaches, and 3) test the influence of other factors in the performance, such as the number of classes or the bag size. The results of these experiments are promising, but further research should be encouraged for studying more complex data configurations.

ACS Style

María Pérez-Ortiz; Pedro Antonio Gutiérrez; M. Carbonero-Ruz; C. Hervas-Martinez. Adapting linear discriminant analysis to the paradigm of learning from label proportions. 2016 IEEE Symposium Series on Computational Intelligence (SSCI) 2016, 1 -7.

AMA Style

María Pérez-Ortiz, Pedro Antonio Gutiérrez, M. Carbonero-Ruz, C. Hervas-Martinez. Adapting linear discriminant analysis to the paradigm of learning from label proportions. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 2016; ():1-7.

Chicago/Turabian Style

María Pérez-Ortiz; Pedro Antonio Gutiérrez; M. Carbonero-Ruz; C. Hervas-Martinez. 2016. "Adapting linear discriminant analysis to the paradigm of learning from label proportions." 2016 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 1-7.

Journal article
Published: 01 December 2016 in Neural Networks
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Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

ACS Style

M. Pérez-Ortiz; P.A. Gutiérrez; M. Carbonero-Ruz; C. Hervás-Martínez. Semi-supervised learning for ordinal Kernel Discriminant Analysis. Neural Networks 2016, 84, 57 -66.

AMA Style

M. Pérez-Ortiz, P.A. Gutiérrez, M. Carbonero-Ruz, C. Hervás-Martínez. Semi-supervised learning for ordinal Kernel Discriminant Analysis. Neural Networks. 2016; 84 ():57-66.

Chicago/Turabian Style

M. Pérez-Ortiz; P.A. Gutiérrez; M. Carbonero-Ruz; C. Hervás-Martínez. 2016. "Semi-supervised learning for ordinal Kernel Discriminant Analysis." Neural Networks 84, no. : 57-66.

Conference paper
Published: 03 November 2016 in 2016 International Joint Conference on Neural Networks (IJCNN)
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Melanoma is a type of cancer that usually occurs on the skin. Early detection is crucial for ensuring five-year survival (which varies between 15% and 99% depending on the melanoma stage). Melanoma severity is typically diagnosed by invasive methods (e.g. a biopsy). In this paper, we propose an alternative system combining image analysis and machine learning for detecting melanoma presence and severity. The 86 features selected consider the shape, colour, pigment network and texture of the melanoma. As opposed to previous studies that have focused on distinguishing melanoma and non-melanoma images, our work considers a finer-grain classification problem using five categories: benign lesions and 4 different stages of melanoma. The dataset presents two main characteristics that are approached by specific machine learning methods: 1) the classes representing melanoma severity follow a natural order, and 2) the dataset is imbalanced, where benign lesions clearly outnumber melanoma ones. Different nominal and ordinal classifiers are considered, one of them being based on an ordinal cascade decomposition method. The cascade method is shown to obtain good performance for all classes, while respecting and exploiting the order information. Moreover, we explore the alternative of applying a class balancing technique, presenting good synergy with the ordinal and nominal methods.

ACS Style

María Pérez-Ortiz; A. Saez; Javier Sanchez-Monedero; Pedro Antonio Gutiérrez; C. Hervas-Martinez. Tackling the ordinal and imbalance nature of a melanoma image classification problem. 2016 International Joint Conference on Neural Networks (IJCNN) 2016, 2156 -2163.

AMA Style

María Pérez-Ortiz, A. Saez, Javier Sanchez-Monedero, Pedro Antonio Gutiérrez, C. Hervas-Martinez. Tackling the ordinal and imbalance nature of a melanoma image classification problem. 2016 International Joint Conference on Neural Networks (IJCNN). 2016; ():2156-2163.

Chicago/Turabian Style

María Pérez-Ortiz; A. Saez; Javier Sanchez-Monedero; Pedro Antonio Gutiérrez; C. Hervas-Martinez. 2016. "Tackling the ordinal and imbalance nature of a melanoma image classification problem." 2016 International Joint Conference on Neural Networks (IJCNN) , no. : 2156-2163.

Conference paper
Published: 08 September 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
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Learning from label proportions is the term used for the learning paradigm where the training data is provided in groups (or “bags”), and only the label proportion for each bag is known. The objective is to learn a model to predict the class labels of individual instances. This paradigm presents very different applications, specially concerning anonymous data. Two different iterative strategies are proposed to deal with this type of problems, both based on optimising the class membership of the instances using the estimated pattern distribution per bag and the label proportions. Discriminant analysis is reformulated to deal with non-crisp class memberships. A thorough set of experiments is conducted to test: (1) the performance gap between these approaches and the fully supervised setting, (2) the potential advantages of optimising class memberships by our proposals, and (3) the influence of factors such as the bag size and the number of classes of the problem in the performance.

ACS Style

M. Pérez-Ortiz; Pedro Antonio Gutiérrez; M. Carbonero-Ruz; C. Hervás-Martínez. Learning from Label Proportions via an Iterative Weighting Scheme and Discriminant Analysis. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 79 -88.

AMA Style

M. Pérez-Ortiz, Pedro Antonio Gutiérrez, M. Carbonero-Ruz, C. Hervás-Martínez. Learning from Label Proportions via an Iterative Weighting Scheme and Discriminant Analysis. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():79-88.

Chicago/Turabian Style

M. Pérez-Ortiz; Pedro Antonio Gutiérrez; M. Carbonero-Ruz; C. Hervás-Martínez. 2016. "Learning from Label Proportions via an Iterative Weighting Scheme and Discriminant Analysis." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 79-88.