Browsing by Author "Fdez-Riverola, Florentino"
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- Active and Assisted Living Ecosystem for the ElderlyPublication . Marcelino, Isabel; Laza, Rosalía; Domingues, Patrício; Gómez-Meire, Silvana; Fdez-Riverola, Florentino; Pereira, AntónioA novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors.
- Quadcriteria Optimization of Binary Classifiers: Error Rates, Coverage, and ComplexityPublication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Ruano-Ordás, David; Zhao, Jiaqi; Fdez-Riverola, Florentino; Ramón Méndez, José; Emmerich, Michael T. M.This paper presents a 4-objective evolutionary multiobjective optimization study for optimizing the error rates (false positives, false negatives), reliability, and complexity of binary classifiers. The example taken is the email anti-spam filtering problem. The two major goals of the optimization is to minimize the error rates that is the false negative rate and the false positive rate. Our approach discusses three-way classification, that is the binary classifier can also not classify an instance in cases where there is not enough evidence to assign the instance to one of the two classes. In this case the instance is marked as suspicious but still presented to the user. The number of unclassified (suspicious) instances should be minimized, as long as this does not lead to errors. This will be termed the coverage objective. The set (ensemble) of rules needed for the anti-spam filter to operate in optimal conditions is addressed as a fourth objective. All objectives stated above are in general conflicting with each other and that is why we address the problem as a 4-objective (quadcriteria) optimization problem. We assess the performance of a set of state-of-the-art evolutionary multiobjective optimization algorithms. These are NSGA-II, SPEA2, and the hypervolume indicator-based SMS-EMOA. Focusing on the anti-spam filter optimization, statistical comparisons on algorithm performance are provided on several benchmarks and a range of performance indicators. Moreover, the resulting 4-D Pareto hyper-surface is discussed in the context of binary classifier optimization.
- RuleSIM: a toolkit for simulating the operation and improving throughput of rule-based spam filtersPublication . Ruano-Ordás, David; Fdez-Glez, Jorge; Fdez-Riverola, Florentino; Basto-Fernandes, Vitor; Méndez, José RamónThis paper introduces RuleSIM, a toolkit comprising different simulation tools specifically designed to aid researchers concerned about spam-filtering throughput. RuleSIM allows easily designing, developing, simulating and comparing new scheduling heuristics using different filters and sets of e-mails. Simulation results can be both graphically analysed, by using different complementary views, and quantitatively compared through several measures. Moreover, the underlying RuleSIM API can be easily integrated with third-party Java optimization platforms to facilitate debugging and achieve better configurations for rule scheduling. RuleSIM is free software distributed under the terms of GNU Lesser General Public License, and both source code and documentation are publicly available at https://github.com/rulesim/v2.0. Copyright © 2015 John Wiley & Sons, Ltd.
- A spam filtering multi-objective optimization study covering parsimony maximization and three-way classificationPublication . Basto-Fernandes, Vitor; Yevseyeva, Iryna; Méndez, José R.; Zhao, Jiaqi; Fdez-Riverola, Florentino; Emmerich, Michael T.M.Classifier performance optimization in machine learning can be stated as a multi-objective optimization problem. In this context, recent works have shown the utility of simple evolutionary multi-objective algorithms (NSGA-II, SPEA2) to conveniently optimize the global performance of different anti-spam filters. The present work extends existing contributions in the spam filtering domain by using three novel indicator-based (SMS-EMOA, CH-EMOA) and decomposition-based (MOEA/D) evolutionary multiobjective algorithms. The proposed approaches are used to optimize the performance of a heterogeneous ensemble of classifiers into two different but complementary scenarios: parsimony maximization and e-mail classification under low confidence level. Experimental results using a publicly available standard corpus allowed us to identify interesting conclusions regarding both the utility of rule-based classification filters and the appropriateness of a three-way classification system in the spam filtering domain.
- Unobstructive Body Area Networks (BAN) for Efficient Movement MonitoringPublication . Felisberto, Filipe; Costa, Nuno; Fdez-Riverola, Florentino; Pereira, AntónioThe technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users’ quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users’ daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN’s nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user.