Browsing by Author "Onieva, Enrique"
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Item Ensemble classification for imbalanced data based on feature space partitioning and hybrid metaheuristics(2019-08-15) Lopez-Garcia, Pedro; Masegosa, Antonio D.; Osaba, Eneko; Onieva, Enrique; Perallos, Asier; QuantumOne of the most challenging issues when facing a classification problem is to deal with imbalanced datasets. Recently, ensemble classification techniques have proven to be very successful in addressing this problem. We present an ensemble classification approach based on feature space partitioning for imbalanced classification. A hybrid metaheuristic called GACE is used to optimize the different parameters related to the feature space partitioning. To assess the performance of the proposal, an extensive experimentation over imbalanced and real-world datasets compares different configurations and base classifiers. Its performance is competitive with that of reference techniques in the literature.Item Fuzzy Logic Strategy for Traction Control in Parallel Hybrid Vehicles(Institute of Electrical and Electronics Engineers Inc., 2023) Gómez-Barroso, Álvaro; Otaola, Eneko; Onieva, Enrique; Arteta, Beñat; Pérez, Joshué; Prieto, Pablo; POWERTRAIN; CCAMDiminishing the environmental impact of the automotive industry is imperative in light of the pivotal role played by this sector in the surge of Greenhouse Gas emissions during the near past. The implementation of novel alternative power-train architectures merging different energy sources, seems to be a viable approach for decreasing reliance on fossil fuels. A parallel hybrid electric heavy-duty vehicle has been considered for this research, where achieving an optimal real-time torque distribution between both energy sources is crucial to maximize system efficiency. An improvement of the currently defined hybridization control strategy in the LONGRUN framework has been proposed considering the required torque, battery state of charge and throttle rate as input variables of a Fuzzy Logic Controller. Total vehicle fuel consumption along four certified drive cycles has been assessed, effectively revealing the feasibility of implementing the suggested control strategy in order to reduce fuel consumption consequently improving the efficiency of the vehicle under consideration.Item Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis(2020) Zhang, Xiao; Onieva, Enrique; Perallos, Asier; Osaba, Eneko; QuantumAccurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.Item A hybrid optimizer based on backtracking search and differential evolution for continuous optimization(2022) Kuyu, Yiğit Çağatay; Onieva, Enrique; Lopez-Garcia, Pedro; Tecnalia Research & InnovationThis paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.Item Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring(2019) Canizo, Mikel; Conde, Angel; Charramendieta, Santiago; Minon, Raul; Cid-Fuentes, Raul G.; Onieva, Enrique; HPAThe emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.Item Preface(2015) Onieva, Enrique; Santos, Igor; Osaba, Eneko; Quintián, Héctor; Corchado, Emilio; QuantumItem Special issue HAIS 2015: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems(2019-01-31) Bringas, Pablo García; Santos, Igor; Onieva, Enrique; Osaba, Eneko; Quintián, Héctor; Corchado, Emilio; Quantum