Browsing by Subject "Feature Selection"

Now showing 1 - 4 of 4
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    Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction
    (Elsevier, 2020-04) Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Sánchez, Gracia; Marín, David; Palacios, Francisco; López, Lucia; Ingeniería de la Información y las Comunicaciones
    AAntimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-ofthe-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision- making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE=(0.1349, 0.1304, 0.1325) and a MAE=(0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.
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    Multivariate feature ranking with high-dimensional data for classification tasks
    (2022-06-08) Jiménez Barrionuevo, Fernando; Sanchez Carpena, G.; Palma Méndez, José Tomás; Miralles Pechuan, L.; Botia Blaya, J. A.; Ingeniería de la Información y las Comunicaciones
    In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on pairwise correlation and pairwise consistency, which havebeen applied for cancer gene expression and genotype-tissue expression classification tasks using public datasets. We statistically proved that the proposed methods outperform the state-of-the-art feature ranking methods Clustering Variation, Chi Squared, Correlation, Information Gain, ReliefF and Significance, as well as other feature selection methods for attribute subset evaluation based on correlation and consistency with the multi-objective evolutionary search strategy, and with the embedded feature selection methods C4.5 and LASSO. The proposed methods have been implemented on the WEKA platform for public use, making all the results reported in this paper repeatable and replicable.
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    Surrogate-assisted multi-objective evolutionary feature selection of generation-based fixed evolution control for time series forecasting with LSTM networks
    (Elsevier, 2024-05-15) Espinosa Fernández, Raquel; Jiménez Barrionuevo, Fernando; Palma Méndez, José Tomás; Ingeniería de la Información y las Comunicaciones
    Surrogate-assisted multi-objective evolutionary algorithms are powerful techniques to solve computationally expensive multi-objective optimization problems. In this paper, we propose a direct fitness replacement method with generation-based fixed evolution control to implement a multi-objective evolutionary algorithm that uses a surrogate model for wrapper-type feature selection, where long short-term memory is established as the learning algorithm. The importance of the work and its benefits lie in the need to reduce the excessive computational time required by conventional wrapper-type feature selection methods based on multi-objective evolutionary algorithms and LSTM networks, maintaining or improving the predictive capacity of the models. We analyze the use of incremental learning to update the surrogate model, in comparison with the conventional non-incremental learning approach. We applied these methods in real-life time series forecasting of air quality, indoor temperature in a smart building and oil temperature in electricity transformers. Multi-step ahead predictions of the forecast models obtained with different meta-learners of the surrogate model were compared by using the Diebold–Mariano statistical test on a multi-criteria performance metric. The proposed method outperformed other approaches for feature selection including, among others, methods based on surrogate-assisted multi-objective evolutionary algorithms developed by the authors in previous research, other surrogate-assisted deterministic methods for feature selection and the conventional wrapper-type feature selection method based on LSTM, improving the prediction on test dataset by 23.98%, 34.61% and 13.77%, respectively.
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    Towards semi-automatic human performance evaluation: The case study of a contact center
    (IOS Press, 2018-06-27) Brunello, Andrea; Jiménez Barrionuevo, Fernando; Marzano, Enrico; Palma Méndez, José Tomás; Sánchez Carpena, Gracia; Sciavicco, Guido; Ingeniería de la Información y las Comunicaciones; Department of Mathematics, Physics, and Computer Science, University of Udine, Udine, Italy; R&D Department, Gap Srlu, Trieste, Italy; Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
    Evaluating in a correct, fair, systematic and reliable way the quality of the work is a central problem in modern business. Both from the psychological and the social point of view, this problem is very far away from being solved, let alone from being managed by a (semi-) automatic decision support system. In this paper we consider the case study of evaluating the operators’ work quality in a medium-sized contact center, and, in particular, the problem of selecting the correct variables to be used in such an evaluation. Starting from a data set representative of the company’s range and size of activities, that allowed no usable predictive model for evaluating the skills of the agents, we were able to devise a reproducible methodology, along with an a posteriori optimization process, to select the essential variables that should be used to objectively evaluate the quality of the agents’ work. These results may be used in a support system helping the supervisors in evaluating the agents’ performances. Moreover, we believe that our methodology may be extrapolated and reused in other comparable contexts characterized by the measurability of the human operators’ performance.