DeustoTeka Examinando por Autor "Onieva Caracuel, Enrique"

Examinando por Autor "Onieva Caracuel, Enrique"

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An adaptive local search with prioritized tracking for Dynamic Environments
(Springer Science and Business Media B.V., 2015-12-01) Masegosa Arredondo, Antonio David; Onieva Caracuel, Enrique; López García, Pedro; Osaba, Eneko; Perallos Ruiz, Asier
Dynamic Optimization Problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher diversity allows a better detection and tracking of changes. However, recent studies have shown that trajectory-based methods can also provide competitive results. This work is focused on this last type of algorithms. Concretely, it proposes a new adaptive local search for continuous DOPs that incorporates a memory archive. The main novelties of the proposal are two-fold: the prioritized tracking, a method to determine which solutions in the memory archive should be tracked first; and an adaptive mechanism to control the minimum step-length or precision of the search. The experimentation done over the Moving Peaks Problem (MPB) shows the benefits of the prioritized tracking and the adaptive precision mechanism. Furthermore, our proposal obtains competitive results with respect to state-of-the-art algorithms for the MPB, both in terms of performance and tracking ability.
An analysis of heuristic metrics for classifier ensemble pruning based on ordered aggregation
(Elsevier Ltd, 2022-04) Elsayed, Amgad Monir Mohamed ; Onieva Caracuel, Enrique; Woźniak, Michał; Martínez Muñoz, Gonzalo
Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning process. The analyzed metrics are based on modifying the order of the classifiers in the bagging algorithm, with selecting the first set in the queue. Some of these criteria include general accuracy, the complementarity of decisions, ensemble diversity, the margin of samples, minimum redundancy, discriminant classifiers, and margin hybrid diversity. The efficacy of those metrics is affected by the original ensemble size, the required subensemble size, the kind of individual classifiers, and the number of classes. While the efficiency is measured in terms of the computational cost and the memory space requirements. The performance of those metrics is assessed over fifteen binary and fifteen multiclass benchmark classification tasks, respectively. In addition, the behavior of those metrics against randomness is measured in terms of the distribution of their accuracy around the median. Results show that ordered aggregation is an efficient strategy to generate subensembles that improve both predictive performance as well as computational and memory complexities of the whole bagging ensemble.
An analysis of heuristic metrics for classifier ensemble pruning based on ordered aggregation
(Elsevier Ltd, 2022-04) Elsayed, Amgad Monir Mohamed ; Onieva Caracuel, Enrique; Woźniak, Michał ; Martínez Muñoz, Gonzalo
Classifier ensemble pruning is a strategy through which a subensemble can be identified via optimizing a predefined performance criterion. Choosing the optimum or suboptimum subensemble decreases the initial ensemble size and increases its predictive performance. In this article, a set of heuristic metrics will be analyzed to guide the pruning process. The analyzed metrics are based on modifying the order of the classifiers in the bagging algorithm, with selecting the first set in the queue. Some of these criteria include general accuracy, the complementarity of decisions, ensemble diversity, the margin of samples, minimum redundancy, discriminant classifiers, and margin hybrid diversity. The efficacy of those metrics is affected by the original ensemble size, the required subensemble size, the kind of individual classifiers, and the number of classes. While the efficiency is measured in terms of the computational cost and the memory space requirements. The performance of those metrics is assessed over fifteen binary and fifteen multiclass benchmark classification tasks, respectively. In addition, the behavior of those metrics against randomness is measured in terms of the distribution of their accuracy around the median. Results show that ordered aggregation is an efficient strategy to generate subensembles that improve both predictive performance as well as computational and memory complexities of the whole bagging ensemble.
Autoencoder-enhanced clustering: a dimensionality reduction approach to financial time series
(Institute of Electrical and Electronics Engineers Inc., 2024-02-05) González Cortés, Daniel Alejandro; Onieva Caracuel, Enrique; Pastor López, Iker; Trinchera, Laura; Wu, Jian
While Machine Learning significantly boosts the performance of predictive models, its efficacy varies across different data dimensions. It is essential to cluster time series data of similar characteristics, particularly in the financial sector. However, clustering financial time series data poses considerable challenges due to the market's inherent complexity and multidimensionality. To address these issues, our study introduces a novel clustering framework that leverages autoencoders for a compressed yet informative representation of financial time series. We rigorously evaluate our approach through multiple dimensionality reduction and clustering algorithms, applying it to key financial indices, including IBEX-35, CAC-40, DAX-30, S&P 500, and FTSE 100. Our findings consistently demonstrate that incorporating autoencoders significantly enhances the granularity and quality of clustering, effectively isolating distinct categories of financial time series. Our findings carry significant ramifications for the financial industry. By refining clustering methodologies, we set the stage for increasingly accurate financial predictive models, offering valuable insights for optimizing investment strategies and enhancing risk management.
A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction
(Elsevier B.V., 2016) Onieva Caracuel, Enrique; López García, Pedro ; Masegosa Arredondo, Antonio David ; Osaba, Eneko ; Perallos Ruiz, Asier
Accurate estimation of the future state of the traffic is an attracting area for researchers in the field of Intelligent Transportation Systems (ITS). This kind of predictions can lead to traffic managers and drivers to act in consequence, reducing the economic and social impact of a possible congestion. Due to the inter-urban traffic information nature, the task of predicting the future state of the traffic requires, in most cases, a non-linear patterns search in the input data. In recent years, a wide variety of models has been used to solve this problem in the most accurate way. Due to that, models generated to provide information about the future state of the road are, usually, incomprehensible to a human operator, making impossible to give him/her an explanation about the causes of the prediction. Given the capacity of rule based systems to explain the reasoning followed to classify a new pattern, the advantages and disadvantages of such approaches are explored in this work. To conduct such task, datasets recorded from the California Department of Transportation are created. A 9-kilometer section of the I5 highway of Sacramento is used for this research. Two different types of datasets are built for the experimentation. One of them contains the entire information recorded. The other one contains with a simplified version of the information, considering only the first, middle and last monitored points of the road. Twelve prediction horizons, from 5 to 60 minutes, were considered for prediction. An experimental comparative study involving 16 state of the art techniques is performed. Techniques tested include those that fall within the categories of Evolutionary Crisp Rule Learning (ECRL) and Evolutionary Fuzzy Rule Learning (EFRL). These methods were selected since they offer to the final user, not only a prediction, but also a legible model about the way in which the decision was taken. Techniques are compared in terms of accuracy and complexity of the models generated.
Conditional random field-based offline map matching for indoor environments
(MDPI AG, 2016-08-16) Bataineh, Safaa Ahmed Hijris; Bahillo, Alfonso ; Díez Blanco, Luis Enrique ; Onieva Caracuel, Enrique ; Bataineh, Ikram
In this paper, we present an offline map matching technique designed for indoor localization systems based on conditional random fields (CRF). The proposed algorithm can refine the results of existing indoor localization systems and match them with the map, using loose coupling between the existing localization system and the proposed map matching technique. The purpose of this research is to investigate the efficiency of using the CRF technique in offline map matching problems for different scenarios and parameters. The algorithm was applied to several real and simulated trajectories of different lengths. The results were then refined and matched with the map using the CRF algorithm.
Creación coral de una asignatura de ciencia de datos online
(Grupo de Comunicación Loyola, 2022) Onieva Caracuel, Enrique; Méndez Zorrilla, Amaia; Fajardo Calderín, Jenny; Pastor López, Iker; Emaldi, Mikel ; Sanz Urquijo, Borja; Eguíluz, Andoni
Design and field experimentation of a cooperative its architecture based on distributed RSUs
(MDPI AG, 2016-07-22) Moreno Emborujo, Asier ; Osaba, Eneko ; Onieva Caracuel, Enrique ; Perallos Ruiz, Asier ; Iovino, Giovanni; Fernández Muga, Pablo
This paper describes a new cooperative Intelligent Transportation System architecture that aims to enable collaborative sensing services. The main goal of this architecture is to improve transportation efficiency and performance. The system, which has been proven within the participation in the ICSI (Intelligent Cooperative Sensing for Improved traffic efficiency) European project, encompasses the entire process of capture and management of available road data. For this purpose, it applies a combination of cooperative services and methods for data sensing, acquisition, processing and communication amongst road users, vehicles, infrastructures and related stakeholders. Additionally, the advantages of using the proposed system are exposed. The most important of these advantages is the use of a distributed architecture, moving the system intelligence from the control centre to the peripheral devices. The global architecture of the system is presented, as well as the software design and the interaction between its main components. Finally, functional and operational results observed through the experimentation are described. This experimentation has been carried out in two real scenarios, in Lisbon (Portugal) and Pisa (Italy).
A driverless vehicle demonstration on motorways and in urban environments
(Taylor and Francis, 2015-01-28) Godoy, Jorge; Pérez Rastelli, Joshué; Onieva Caracuel, Enrique; Villagrá Serrano, Jorge; Milanés, Vicente; Haber Guerra, Rodolfo Elías
The constant growth of the number of vehicles in today's world demands improvements in the safety and efficiency of roads and road use. This can be in part satisfied by the implementation of autonomous driving systems because of their greater precision than human drivers in controlling a vehicle. As result, the capacity of the roads would be increased by reducing the spacing between vehicles. Moreover, greener driving modes could be applied so that the fuel consumption, and therefore carbon emissions, would be reduced. This paper presents the results obtained by the AUTOPIA program during a public demonstration performed in June 2012. This driverless experiment consisted of a 100-kilometre route around Madrid (Spain), including both urban and motorway environments. A first vehicle - acting as leader and manually driven - transmitted its relevant information - i.e., position and speed - through an 802.11p communication link to a second vehicle, which tracked the leader's trajectory and speed while maintaining a safe distance. The results were encouraging, and showed the viability of the AUTOPIA approach.
Dynamic frame update policy for UHF RFID sensor tag collisions
(MDPI AG, 2020-05-09) Arjona Aguilera, Laura; Landaluce, Hugo; Perallos Ruiz, Asier; Onieva Caracuel, Enrique
The current growing demand for low-cost edge devices to bridge the physical–digital divide has triggered the growing scope of Radio Frequency Identification (RFID) technology research. Besides object identification, researchers have also examined the possibility of using RFID tags for low-power wireless sensing, localisation and activity inference. This paper focuses on passive UHF RFID sensing. An RFID system consists of a reader and various numbers of tags, which can incorporate different kinds of sensors. These sensor tags require fast anti-collision protocols to minimise the number of collisions with the other tags sharing the reader’s interrogation zone. Therefore, RFID application developers must be mindful of anti-collision protocols. Dynamic Frame Slotted Aloha (DFSA) anti-collision protocols have been used extensively in the literature because EPCglobal Class 1 Generation 2 (EPC C1G2), which is the current communication protocol standard in RFID, employs this strategy. Protocols under this category are distinguished by their policy for updating the transmission frame size. This paper analyses the frame size update policy of DFSA strategies to survey and classify the main state-of-the-art of DFSA protocols according to their policy. Consequently, this paper proposes a novel policy to lower the time to read one sensor data packet compared to existing strategies. Next, the novel anti-collision protocol Fuzzy Frame Slotted Aloha (FFSA) is presented, which applies this novel DFSA policy. The results of our simulation confirm that FFSA significantly decreases the sensor tag read time for a wide range of tag populations when compared to earlier DFSA protocols thanks to the proposed frame size update policy.
Fast fuzzy anti-collision protocol for the RFID standard EPC Gen-2
(Institution of Engineering and Technology, 2016-04-14) Arjona Aguilera, Laura ; Landaluce, Hugo ; Perallos Ruiz, Asier ; Onieva Caracuel, Enrique
A new methodology which integrates fuzzy logic with RFID anti-collision protocols is proposed. The resulting FuzzyQ protocol significantly decreases the identification time by updating the transmission frame size in a dynamic and adaptive way. Simulation results show the performance of FuzzyQ compared with earlier protocols based on the standard EPC Gen-2.
Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis
(Inderscience Publishers, 2020) Zhang, Xiaoxiao; Onieva Caracuel, Enrique; Perallos Ruiz, Asier; Osaba, Eneko
Accurate 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.
Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems
(Elsevier B.V., 2018-01-03) Osaba, Eneko ; Carballedo Morillo, Roberto ; Díaz Martín, José Fernando ; Onieva Caracuel, Enrique ; Masegosa Arredondo, Antonio David ; Perallos Ruiz, Asier
Researchers who investigate in any area related to computational algorithms (both defining new algorithms or improving existing ones) usually find large difficulties to test their work. Comparisons among different researches in this field are often a hard task, due to the ambiguity or lack of detail in the presentation of the work and its results. On many occasions, the replication of the work conducted by other researchers is required, which leads to a waste of time and a delay in the research advances. The authors of this study propose a procedure to introduce new techniques and their results in the field of routing problems. In this paper, this procedure is detailed, and a set of good practices to follow are deeply described. It is noteworthy that this procedure can be applied to any combinatorial optimization problem. Anyway, the literature of this study is focused on routing problems. This field has been chosen because of its importance in real world, and its relevance in the actual literature.
A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
(Elsevier Ltd, 2020-03) Bogaerts, Toon; Masegosa Arredondo, Antonio David; Angarita Zapata, Juan S.; Onieva Caracuel, Enrique; Hellinckx, Peter
Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.
Multi-head CNN–RNN for multi-time series anomaly detection: an industrial case study
(Elsevier B.V., 2019-10-21) Canizo, Mikel; Triguero, Isaac; Conde, Ángel; Onieva Caracuel, Enrique
Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of different nature, scales and frequencies from the same machine. Traditionally, machine learning techniques require a separate data pre-processing before training, which tends to be very time-consuming and often requires domain knowledge. Recent deep learning approaches have shown to perform well on raw time series data, eliminating the need for pre-processing. In this work, we propose a deep learning based approach for supervised multi-time series anomaly detection that combines a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) in different ways. Unlike other approaches, we use independent CNNs, so-called convolutional heads, to deal with anomaly detection in multi-sensor systems. We address each sensor individually avoiding the need for data pre-processing and allowing for a more tailored architecture for each type of sensor. We refer to this architecture as Multi-head CNN–RNN. The proposed architecture is assessed against a real industrial case study, provided by an industrial partner, where a service elevator is monitored. Within this case study, three type of anomalies are considered: point, context-specific, and collective.The experimental results show that the proposed architecture is suitable for multi-time series anomaly detection as it obtained promising results on the real industrial scenario.
A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem
(Elsevier Ltd, 2024-10-15) Rodríguez Esparza, Erick; Masegosa Arredondo, Antonio David; Oliva, Diego; Onieva Caracuel, Enrique
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.
A novel software architecture for the provision of context-aware semantic transport information
(MDPI AG, 2015-05-26) Moreno Emborujo, Asier; Perallos Ruiz, Asier; López de Ipiña González de Artaza, Diego; Onieva Caracuel, Enrique; Salaberria Larrauri, Itziar; Masegosa Arredondo, Antonio David
The effectiveness of Intelligent Transportation Systems depends largely on the ability to integrate information from diverse sources and the suitability of this information for the specific user. This paper describes a new approach for the management and exchange of this information, related to multimodal transportation. A novel software architecture is presented, with particular emphasis on the design of the data model and the enablement of services for information retrieval, thereby obtaining a semantic model for the representation of transport information. The publication of transport data as semantic information is established through the development of a Multimodal Transport Ontology (MTO) and the design of a distributed architecture allowing dynamic integration of transport data. The advantages afforded by the proposed system due to the use of Linked Open Data and a distributed architecture are stated, comparing it with other existing solutions. The adequacy of the information generated in regard to the specific user’s context is also addressed. Finally, a working solution of a semantic trip planner using actual transport data and running on the proposed architecture is presented, as a demonstration and validation of the system.
Optimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentation
(Institute of Electrical and Electronics Engineers Inc., 2024) Rodríguez Esparza, Erick; Ramos Soto, Oscar; Masegosa Arredondo, Antonio David; Onieva Caracuel, Enrique; Oliva, Diego; Arriandiaga Laresgoiti, Ander; Ghosh, Arka
Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD's applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD's effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature
Portfolio construction using explainable reinforcement learning
(John Wiley and Sons Inc, 2024-11) Cortés González, Daniel; Onieva Caracuel, Enrique; Pastor López, Iker; Trinchera, Laura; Wu, Jian
While machine learning's role in financial trading has advanced considerably, algorithmic transparency and explainability challenges still exist. This research enriches prior studies focused on high-frequency financial data prediction by introducing an explainable reinforcement learning model for portfolio management. This model transcends basic asset prediction, formulating concrete, actionable trading strategies. The methodology is applied in a custom trading environment mimicking the CAC-40 index's financial conditions, allowing the model to adapt dynamically to market changes based on iterative learning from historical data. Empirical findings reveal that the model outperforms an equally weighted portfolio in out-of-sample tests. The study offers a dual contribution: it elevates algorithmic planning while significantly boosting transparency and interpretability in financial machine learning. This approach tackles the enduring ‘black-box’ issue and provides a holistic, transparent framework for managing investment portfolios.
Selective ensemble of classifiers trained on selective samples
(Elsevier B.V., 2022-04-14) Elsayed, Amgad Monir Mohamed ; Onieva Caracuel, Enrique; Woźniak, Michał
Classifier ensembles are characterized by the high quality of classification, thanks to their generalizing ability. Most existing ensemble algorithms use all learning samples to learn the base classifiers that may negatively impact the ensemble's diversity. Also, the existing ensemble pruning algorithms often return suboptimal solutions that are biased by the selection criteria. In this work, we present a proposal to alleviate these drawbacks. We employ an instance selection method to query a reduced training set that reduces both the space complexity of the formed ensemble members and the time complexity to classify an instance. Additionally, we propose a guided search-based pruning schema that perfectly explores large-size ensembles and brings on a near-optimal subensemble with less computational requirements in reduced memory space and improved prediction time. We show experimentally how the proposed method could be an alternative to large-size ensembles. We demonstrate how to form less-complex, small-size, and high-accurate ensembles through our proposal. Experiments on 25 datasets show that the proposed method can produce effective ensembles better than Random Forest and baseline classifier pruning methods. Moreover, our proposition is comparable with the Extreme Gradient Boosting Algorithm in terms of accuracy.

Examinando por Autor "Onieva Caracuel, Enrique"

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