Comparison of machine learning models applied on anonymized data with different techniques

Anonymization techniques based on obfuscating the quasi-identifiers by means of value generalization hierarchies are widely used to achieve preset levels of privacy. To prevent different types of attacks against database privacy it is necessary to apply several anonymization techniques beyond the classical k-anonymity or $\ell$-diversity. However, the application of these methods is directly connected to a reduction of their utility in prediction and decision making tasks. In this work we study four classical machine learning methods currently used for classification purposes in order to analyze the results as a function of the anonymization techniques applied and the parameters selected for each of them. The performance of these models is studied when varying the value of k for k-anonymity and additional tools such as $\ell$-diversity, t-closeness and $\delta$-disclosure privacy are also deployed on the well-known adult dataset.

Frouros: A Python library for drift detection in Machine Learning problems

Frouros is a Python library capable of detecting drift in machine learning problems. It provides a combination of classical and more recent algorithms for drift detection: both supervised and unsupervised, as well as some capable of acting in a semi-supervised manner. We have designed it with the objective of being easily integrated with the scikit-learn library, implementing the same application programming interface. The library is developed following a set of best development and continuous integration practices to ensure ease of maintenance and extensibility. The source code is available at https://github.com/IFCA/frouros.

Study of the performance and scalability of federated learning for medical imaging with intermittent clients

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner, i.e., they do not all have the same number of data. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two approaches to follow will be analyzed in the case of intermittent clients, as in a real scenario some clients may leave the training, and some new ones may enter the training. The evolution of the results for the test set in terms of accuracy, area under the curve and execution time is shown as the number of clients into which the original data is divided increases. Finally, improvements and future work in the field are proposed.

Study of the performance and scalablity of federated learning for medical imaging with intermittent clients

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner, i.e., they do not all have the same number of data. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two approaches to follow will be analyzed in the case of intermittent clients, as in a real scenario some clients may leave the training, and some new ones may enter the training. The evolution of the results for the test set in terms of accuracy, area under the curve and execution time is shown as the number of clients into which the original data is divided increases. Finally, improvements and future work in the field are proposed.

Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study

In this work we evaluate the applicability of an ensemble of population models and machine learning models to predict the near future evolution of the COVID-19 pandemic, with a particular use case in Spain. We rely solely in open and public datasets, fusing incidence, vaccination, human mobility and weather data to feed our machine learning models (Random Forest, Gradient Boosting, k-Nearest Neighbours and Kernel Ridge Regression). We use the incidence data to adjust classic population models (Gompertz, Logistic, Richards, Bertalanffy) in order to be able to better capture the trend of the data. We then ensemble these two families of models in order to obtain a more robust and accurate prediction. Furthermore, we have observed an improvement in the predictions obtained with machine learning models as we add new features (vaccines, mobility, climatic conditions), analyzing the importance of each of them using Shapley Additive Explanation values. As in any other modelling work, data and predictions quality have several limitations and therefore they must be seen from a critical standpoint, as we discuss in the text. Our work concludes that the ensemble use of these models improves the individual predictions (using only machine learning models or only population models) and can be applied, with caution, in cases when compartmental models cannot be utilized due to the lack of relevant data.

Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In these cases, surrogate measures can be used to make estimations by means of data-driven models. In this work, variables that are commonly measured in-situ are used as surrogates to estimate the concentrations of nutrients in a rural catchment and in an urban one, making use of machine learning models, specifically Random Forests. The results are compared with those of linear modelling using the same number of surrogates, obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1%. The profit from including up to seven surrogate sensors was computed, concluding that adding more than 4 and 5 sensors in each of the catchments respectively was not worthy in terms of error improvement.