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In the last decades, a wide portfolio of Feature Weighting (FW) methods have been proposed in the literature. Their main potential is the capability to transform the features in order to contribute to the Machine Learning (ML) algorithm metric proportionally to their estimated relevance for inferring the output pattern. Nevertheless, the extensive number of FW related works makes difficult to do a scientific study in this field of knowledge. Therefore, in this paper a global taxonomy for FW methods is proposed by focusing on: (1) the learning approach (supervised or unsupervised), (2) the methodology used to calculate the weights (global or local), and (3) the feedback obtained from the ML algorithm when estimating the weights (filter or wrapper). Among the different taxonomy levels, an extensive review of the state-of-the-art is presented, followed by some considerations and guide points for the FW strategies selection regarding significant aspects of real-world data analysis problems. Finally, a summary of conclusions and challenges in the FW field is briefly outlined.
Iratxe Niño-Adan; Diana Manjarres; Itziar Landa-Torres; Eva Portillo. Feature weighting methods: A review. Expert Systems with Applications 2021, 184, 115424 .
AMA StyleIratxe Niño-Adan, Diana Manjarres, Itziar Landa-Torres, Eva Portillo. Feature weighting methods: A review. Expert Systems with Applications. 2021; 184 ():115424.
Chicago/Turabian StyleIratxe Niño-Adan; Diana Manjarres; Itziar Landa-Torres; Eva Portillo. 2021. "Feature weighting methods: A review." Expert Systems with Applications 184, no. : 115424.
Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-of-the-art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with
Iratxe Niño-Adan; Itziar Landa-Torres; Diana Manjarres; Eva Portillo; Lucía Orbe. Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column. Sensors 2021, 21, 3991 .
AMA StyleIratxe Niño-Adan, Itziar Landa-Torres, Diana Manjarres, Eva Portillo, Lucía Orbe. Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column. Sensors. 2021; 21 (12):3991.
Chicago/Turabian StyleIratxe Niño-Adan; Itziar Landa-Torres; Diana Manjarres; Eva Portillo; Lucía Orbe. 2021. "Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column." Sensors 21, no. 12: 3991.
Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.
Diana Manjarres; Lara Mabe; Xabat Oregi; Itziar Landa-Torres. Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level. Sustainability 2019, 11, 1495 .
AMA StyleDiana Manjarres, Lara Mabe, Xabat Oregi, Itziar Landa-Torres. Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level. Sustainability. 2019; 11 (5):1495.
Chicago/Turabian StyleDiana Manjarres; Lara Mabe; Xabat Oregi; Itziar Landa-Torres. 2019. "Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level." Sustainability 11, no. 5: 1495.
Thermal cracking is one of the most energy-consuming process in the chemical industry and its optimization has become a real challenge for the research community. In this context, this paper proposes two metaheuristic approaches based on the Genetic Algorithm (GA) and the Harmony Search (HS) algorithms for minimizing the sum of the Energy Consumption and the Water Use in the overall thermal cracking process. Simulation results show that HS achieves best average minimum and mean values than its counterpart GA.
Fernando Boto; Diana Manjarres; Itziar Landa-Torres. Metaheuristic Optimization of Natural Resources in Thermal Cracking Process. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization 2018, 1409 -1419.
AMA StyleFernando Boto, Diana Manjarres, Itziar Landa-Torres. Metaheuristic Optimization of Natural Resources in Thermal Cracking Process. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. 2018; ():1409-1419.
Chicago/Turabian StyleFernando Boto; Diana Manjarres; Itziar Landa-Torres. 2018. "Metaheuristic Optimization of Natural Resources in Thermal Cracking Process." EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization , no. : 1409-1419.
This paper presents a preliminary metaheuristic approach for underwater swarm robotic parameter configuration applied to optimal plume detection under time-variant scenarios. In this work the plume scanning has followed a collaborative approach that has been modelled following a real-based scenario obtained within the European project SWARMs (“Smart and Networking Underwater Robots in Cooperation Meshes”) [1]. The proposed optimization algorithm is designed aiming at minimising the overall time of the mission while assuring an optimal plume detection. Preliminary results show that this proposed approach can assist the operator when designing the mission and configuring the optimal swarm robotic parameters.
Itziar Landa-Torres; Diana Manjarres; Sonia Bilbao. Metaheuristic Algorithm for Optimal Swarm Robotic Parameter Configuration in Time-Variant Plume Detection. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization 2018, 959 -969.
AMA StyleItziar Landa-Torres, Diana Manjarres, Sonia Bilbao. Metaheuristic Algorithm for Optimal Swarm Robotic Parameter Configuration in Time-Variant Plume Detection. EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization. 2018; ():959-969.
Chicago/Turabian StyleItziar Landa-Torres; Diana Manjarres; Sonia Bilbao. 2018. "Metaheuristic Algorithm for Optimal Swarm Robotic Parameter Configuration in Time-Variant Plume Detection." EngOpt 2018 Proceedings of the 6th International Conference on Engineering Optimization , no. : 959-969.
Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.
Diana Manjarres; Ricardo Alonso; Sergio Gil-Lopez; Itziar Landa-Torres. Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 1 -12.
AMA StyleDiana Manjarres, Ricardo Alonso, Sergio Gil-Lopez, Itziar Landa-Torres. Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():1-12.
Chicago/Turabian StyleDiana Manjarres; Ricardo Alonso; Sergio Gil-Lopez; Itziar Landa-Torres. 2017. "Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 1-12.
In the last years, default prediction systems have become an important tool for a wide variety of financial institutions, such as banking systems or credit business, for which being able of detecting credit and default risks, translates to a better financial status. Nevertheless, small and medium-sized enterprises did not focus its attention on customer default prediction but in maximizing the sales rate. Consequently, many companies could not cope with the customers’ debt and ended up closing the business. In order to overcome this issue, this paper presents a novel decision support system for default prediction specially tailored for small and medium-sized enterprises that retrieves the information related to the customers in an Enterprise Resource Planning (ERP) system and obtain the default risk probability of a new order or client. The resulting approach has been tested in a Graphic Arts printing company of The Basque Country allowing taking prioritized and preventive actions with regard to the default risk probability and the customer’s characteristics. Simulation results verify that the proposed scheme achieves a better performance than a naïve Random Forest (RF) classification technique in real scenarios with unbalanced datasets.
Diana Manjarres; Itziar Landa-Torres; Imanol Andonegui. An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises. Computer Vision 2017, 533 -542.
AMA StyleDiana Manjarres, Itziar Landa-Torres, Imanol Andonegui. An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises. Computer Vision. 2017; ():533-542.
Chicago/Turabian StyleDiana Manjarres; Itziar Landa-Torres; Imanol Andonegui. 2017. "An Intelligent Decision Support System for Assessing the Default Risk in Small and Medium-Sized Enterprises." Computer Vision , no. : 533-542.
Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by devising three heuristic solvers aimed at efficiently scheduling tasks in robotic swarms, which collaborate together to accomplish a mission, and by presenting experimental results obtained over realistic scenarios in the underwater environment. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks and the relative execution order of such tasks within the schedule of certain robots. The obtained results reveal interesting differences in terms of Pareto optimality and spread between the algorithms considered in the benchmark, which are insightful for the selection of a proper task scheduler in real underwater campaigns.
Itziar Landa-Torres; Diana Manjarres; Sonia Bilbao; Javier Del Ser. Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics. Sensors 2017, 17, 762 .
AMA StyleItziar Landa-Torres, Diana Manjarres, Sonia Bilbao, Javier Del Ser. Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics. Sensors. 2017; 17 (4):762.
Chicago/Turabian StyleItziar Landa-Torres; Diana Manjarres; Sonia Bilbao; Javier Del Ser. 2017. "Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics." Sensors 17, no. 4: 762.
We report a critical assessment of the use of an Inverse Design (ID) approach steamed by an improved Harmony Search (IHS) algorithm for enhancing light coupling to densely integrated photonic integratic circuits (PICs) using novel grating structures. Grating couplers, performing as a very attractive vertical coupling scheme for standard silicon nano waveguides are nowadays a custom component in almost every PIC. Nevertheless, their efficiency can be highly enhanced by using our ID methodology that can deal simultaneously with many physical and geometrical parameters. Moreover, this method paves the way for designing more sophisticated non-uniform gratings, which not only match the coupling efficiency of conventional periodic corrugated waveguides, but also allow to devise more complex components such as wavelength or polarization splitters, just to cite some
Imanol Andonegui; Itziar Landa-Torres; Diana Manjarres; Angel J. Garcia-Adeva. Novel Light Coupling Systems Devised Using a Harmony Search Algorithm Approach. Advances in Intelligent Systems and Computing 2017, 294 -303.
AMA StyleImanol Andonegui, Itziar Landa-Torres, Diana Manjarres, Angel J. Garcia-Adeva. Novel Light Coupling Systems Devised Using a Harmony Search Algorithm Approach. Advances in Intelligent Systems and Computing. 2017; ():294-303.
Chicago/Turabian StyleImanol Andonegui; Itziar Landa-Torres; Diana Manjarres; Angel J. Garcia-Adeva. 2017. "Novel Light Coupling Systems Devised Using a Harmony Search Algorithm Approach." Advances in Intelligent Systems and Computing , no. : 294-303.
Nowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies’ selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.
Diana Manjarres; Lara Mabe; Xabat Oregi; Itziar Landa-Torres; Eneko Arrizabalaga. A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale. Advances in Intelligent Systems and Computing 2017, 320 -332.
AMA StyleDiana Manjarres, Lara Mabe, Xabat Oregi, Itziar Landa-Torres, Eneko Arrizabalaga. A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale. Advances in Intelligent Systems and Computing. 2017; ():320-332.
Chicago/Turabian StyleDiana Manjarres; Lara Mabe; Xabat Oregi; Itziar Landa-Torres; Eneko Arrizabalaga. 2017. "A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale." Advances in Intelligent Systems and Computing , no. : 320-332.
In several wireless sensor network applications the availability of accurate nodes' location information is essential to make collected data meaningful. In this context, estimating the positions of all unknown-located nodes of the network based on noisy distance-related measurements (usually referred to as localization) generally embodies a non-convex optimization problem, which is further exacerbated by the fact that the network may not be uniquely localizable, especially when its connectivity degree is not sufficiently high. In order to efficiently tackle this problem, we propose a novel two-objective localization approach based on the combination of the harmony search (HS) algorithm and a local search procedure. Moreover, some connectivity-based geometrical constraints are defined and exploited to limit the areas in which sensor nodes can be located. The proposed method is tested with different network configurations and compared, in terms of normalized localization error and three multi-objective quality indicators, with a state-of-the-art metaheuristic localization scheme based on the Pareto archived evolution strategy (PAES). The results show that the proposed approach achieves considerable accuracies and, in the majority of the scenarios, outperforms PAES
Diana Manjarres; Javier Del Ser; Sergio Gil-Lopez; Massimo Vecchio; Itziar Landa-Torres; Sancho Salcedo-Sanz; Roberto Lopez-Valcarce. On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks. Engineering Applications of Artificial Intelligence 2013, 26, 669 -676.
AMA StyleDiana Manjarres, Javier Del Ser, Sergio Gil-Lopez, Massimo Vecchio, Itziar Landa-Torres, Sancho Salcedo-Sanz, Roberto Lopez-Valcarce. On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks. Engineering Applications of Artificial Intelligence. 2013; 26 (2):669-676.
Chicago/Turabian StyleDiana Manjarres; Javier Del Ser; Sergio Gil-Lopez; Massimo Vecchio; Itziar Landa-Torres; Sancho Salcedo-Sanz; Roberto Lopez-Valcarce. 2013. "On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks." Engineering Applications of Artificial Intelligence 26, no. 2: 669-676.
The availability of accurate location information of constituent nodes becomes essential in many applications of wireless sensor networks. In this context, we focus on anchor-based networks where the position of some few nodes are assumed to be fixed and known a priori, whereas the location of all other nodes is to be estimated based on noisy pairwise distance measurements. This localization task embodies a non-convex optimization problem which gets even more involved by the fact that the network may not be uniquely localizable, especially when its connectivity is not sufficiently high. To efficiently tackle this problem, we present a novel soft computing approach based on a hybridization of the Harmony Search (HS) algorithm with a local search procedure that iteratively alleviates the aforementioned non-uniqueness of sparse network deployments. Furthermore, the areas in which sensor nodes can be located are limited by means of connectivity-based geometrical constraints. Extensive simulation results show that the proposed approach outperforms previously published soft computing localization techniques in most of the simulated topologies. In particular, to assess the effectiveness of the technique, we compare its performance, in terms of Normalized Localization Error (NLE), to that of Simulated Annealing (SA)-based and Particle Swarm Optimization (PSO)-based techniques, as well as a naive implementation of a Genetic Algorithm (GA) incorporating the same local search procedure here proposed. Non-parametric hypothesis tests are also used so as to shed light on the statistical significance of the obtained results.
Diana Manjarres; Javier Del Ser; Sergio Gil-Lopez; Massimo Vecchio; Itziar Landa-Torres; Roberto Lopez-Valcarce. A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks. Soft Computing 2012, 17, 17 -28.
AMA StyleDiana Manjarres, Javier Del Ser, Sergio Gil-Lopez, Massimo Vecchio, Itziar Landa-Torres, Roberto Lopez-Valcarce. A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks. Soft Computing. 2012; 17 (1):17-28.
Chicago/Turabian StyleDiana Manjarres; Javier Del Ser; Sergio Gil-Lopez; Massimo Vecchio; Itziar Landa-Torres; Roberto Lopez-Valcarce. 2012. "A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks." Soft Computing 17, no. 1: 17-28.
Javier Del Ser; Diana Manjarres; Sergio Gil-Lopez; Javier Garcia-Frias; Pedro M. Crespo. Iterative Fusion of Distributed Decisions over the Gaussian Multiple-Access Channel Using Concatenated BCH-LDGM Codes. EURASIP Journal on Wireless Communications and Networking 2011, 2011, 825327 .
AMA StyleJavier Del Ser, Diana Manjarres, Sergio Gil-Lopez, Javier Garcia-Frias, Pedro M. Crespo. Iterative Fusion of Distributed Decisions over the Gaussian Multiple-Access Channel Using Concatenated BCH-LDGM Codes. EURASIP Journal on Wireless Communications and Networking. 2011; 2011 (1):825327.
Chicago/Turabian StyleJavier Del Ser; Diana Manjarres; Sergio Gil-Lopez; Javier Garcia-Frias; Pedro M. Crespo. 2011. "Iterative Fusion of Distributed Decisions over the Gaussian Multiple-Access Channel Using Concatenated BCH-LDGM Codes." EURASIP Journal on Wireless Communications and Networking 2011, no. 1: 825327.