Fuzzy Linear Programming models and methods has been one of the most and well studied topics insi... more Fuzzy Linear Programming models and methods has been one of the most and well studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real world areas. In this paper a basic introduction to the main models and methods in fuzzy mathematical programming, with special emphasis on those developed by the authors, is presented. As a whole, Linear Programming problems with fuzzy costs, fuzzy constraints and fuzzy coefficients in the technological matrix are analyzed. Finally, future research and development lines are also pointed out by focusing on fuzzy sets based heuristic algorithms. MSC: 90C59 RESUMEN Los modelos y métodos de la Programación Lineal Borrosa han sido uno de los tópicos más estudiados dentro de la ampliá area de la llamada Soft Computing. Sus aplicaciones así como sus realizacioens prácticas pueden hallarse en todas lasáreaslas´lasáreas del mundo real. En este trabajo se desarrolla una introducción...
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-... more Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this...
This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming prob... more This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming problem. We propose Genetic Search based approaches to the case in which fuzzy constraints are assumed.
A stock market investor buys and sells stocks in order to obtain the best possible profit. This d... more A stock market investor buys and sells stocks in order to obtain the best possible profit. This dealing can be depicted on a graph, on which each node represents the stocks purchased. In this way, this paper proposes the use of ant colony optimization for calculating the best sequence when buying and selling, thereby helping when deciding how to invest.
Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly ge... more Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. This approach combines the robustness of multi-classifiers, the construction efficiency of decision trees, the power of the randomness to increase the diversity of the trees in the forest, and the flexibility of fuzzy logic and the fuzzy sets for data managing.
A methodology to evaluate the quality of health Web sites is presented. The evaluation methodolog... more A methodology to evaluate the quality of health Web sites is presented. The evaluation methodology is composed of a quality criteria set and a computation instrument to generate quality assessments. The quality criteria set is based on both technical criteria and criteria related with the content of information on the Web sites. Quality assessments are defined using users’ perceptions on the health Web site quality. We assume a fuzzy linguistic modelling to represent the users’ perceptions. The methodology is entirely useroriented, as the quality criteria are derived from the needs expressed by the users, and evaluations of Web sites are calculated from the users point of view.
Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern... more Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. It has a number of advantages over other pattern recognition methods, including simpler methods of attribute selection or ability to learn in real time environments. These methods build a prototype for each attribute and combine the partial estimations of each prototype by a fusion operator. One of the major problems of this technique is that it is not able to model the dependencies between attributes since fuzzy pattern matching assumes non interactivity between them, and nowadays there is no heuristic in the literature that solves this problem. In this paper we propose a solution to this problem. In order to keep the good properties of fuzzy pattern matching, this heuristic will have the objective of minimizing the dependencies between attributes modeled. To show the accuracy of the proposed solution, we have tested the method on several data sets.
In this work, the structure for the prototype construction of an application that can be framed w... more In this work, the structure for the prototype construction of an application that can be framed within ubiquitous sensing is proposed. The objective of application is to allow that a user knows through his mobile device which other users of his environment are doing the same activity as him. Therefore, the knowledge is obtained from data acquired by pervasive sensors. The FIWARE infrastructure is used to allow to homogenize the data flows.
Currently, many of the elements that surround us in daily life need software systems that work fr... more Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous and noisy data, that is, imperfect information.Although this imperfect information is inevitable, most applications have traditionally ignored the need for developing appropriate approaches for representing and reasoning with such data imperfections. The soft computing field has dealt with the development of techniques that can work with this kind of information as discipline whose main characteristic is tolerance to inaccuracy and uncertainty.In this work, we extend the k-Nearest Neighbors technique using concepts and methods provided by Soft Computing. The aim is to carry out the...
Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regressio... more The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNN$$_{imp}$$imp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and real-world datasets with imperfect data. The results, validated through statistical tests, show that the kNN$$_{imp}$$imp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Genetic Algorithms are adaptative proce... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Genetic Algorithms are adaptative procedures of optimization and search inspired in the mechanisms of natural selection and genetic. Currently, these algorithms are being highly considered above all in those problems with ...
Fuzzy Linear Programming models and methods has been one of the most and well studied topics insi... more Fuzzy Linear Programming models and methods has been one of the most and well studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real world areas. In this paper a basic introduction to the main models and methods in fuzzy mathematical programming, with special emphasis on those developed by the authors, is presented. As a whole, Linear Programming problems with fuzzy costs, fuzzy constraints and fuzzy coefficients in the technological matrix are analyzed. Finally, future research and development lines are also pointed out by focusing on fuzzy sets based heuristic algorithms. MSC: 90C59 RESUMEN Los modelos y métodos de la Programación Lineal Borrosa han sido uno de los tópicos más estudiados dentro de la ampliá area de la llamada Soft Computing. Sus aplicaciones así como sus realizacioens prácticas pueden hallarse en todas lasáreaslas´lasáreas del mundo real. En este trabajo se desarrolla una introducción...
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-... more Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this...
This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming prob... more This paper presents a method to solve a linear or nonlinear Fuzzy Multiobjective Programming problem. We propose Genetic Search based approaches to the case in which fuzzy constraints are assumed.
A stock market investor buys and sells stocks in order to obtain the best possible profit. This d... more A stock market investor buys and sells stocks in order to obtain the best possible profit. This dealing can be depicted on a graph, on which each node represents the stocks purchased. In this way, this paper proposes the use of ant colony optimization for calculating the best sequence when buying and selling, thereby helping when deciding how to invest.
Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly ge... more Following Breiman’s methodology, we propose a multi-classifier based on a “forest” of randomly generated fuzzy decision trees, i.e., a Fuzzy Random Forest. This approach combines the robustness of multi-classifiers, the construction efficiency of decision trees, the power of the randomness to increase the diversity of the trees in the forest, and the flexibility of fuzzy logic and the fuzzy sets for data managing.
A methodology to evaluate the quality of health Web sites is presented. The evaluation methodolog... more A methodology to evaluate the quality of health Web sites is presented. The evaluation methodology is composed of a quality criteria set and a computation instrument to generate quality assessments. The quality criteria set is based on both technical criteria and criteria related with the content of information on the Web sites. Quality assessments are defined using users’ perceptions on the health Web site quality. We assume a fuzzy linguistic modelling to represent the users’ perceptions. The methodology is entirely useroriented, as the quality criteria are derived from the needs expressed by the users, and evaluations of Web sites are calculated from the users point of view.
Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern... more Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. It has a number of advantages over other pattern recognition methods, including simpler methods of attribute selection or ability to learn in real time environments. These methods build a prototype for each attribute and combine the partial estimations of each prototype by a fusion operator. One of the major problems of this technique is that it is not able to model the dependencies between attributes since fuzzy pattern matching assumes non interactivity between them, and nowadays there is no heuristic in the literature that solves this problem. In this paper we propose a solution to this problem. In order to keep the good properties of fuzzy pattern matching, this heuristic will have the objective of minimizing the dependencies between attributes modeled. To show the accuracy of the proposed solution, we have tested the method on several data sets.
In this work, the structure for the prototype construction of an application that can be framed w... more In this work, the structure for the prototype construction of an application that can be framed within ubiquitous sensing is proposed. The objective of application is to allow that a user knows through his mobile device which other users of his environment are doing the same activity as him. Therefore, the knowledge is obtained from data acquired by pervasive sensors. The FIWARE infrastructure is used to allow to homogenize the data flows.
Currently, many of the elements that surround us in daily life need software systems that work fr... more Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous and noisy data, that is, imperfect information.Although this imperfect information is inevitable, most applications have traditionally ignored the need for developing appropriate approaches for representing and reasoning with such data imperfections. The soft computing field has dealt with the development of techniques that can work with this kind of information as discipline whose main characteristic is tolerance to inaccuracy and uncertainty.In this work, we extend the k-Nearest Neighbors technique using concepts and methods provided by Soft Computing. The aim is to carry out the...
Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regressio... more The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNN$$_{imp}$$imp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and real-world datasets with imperfect data. The results, validated through statistical tests, show that the kNN$$_{imp}$$imp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection.
CiteSeerX - Document Details (Isaac Councill, Lee Giles): Genetic Algorithms are adaptative proce... more CiteSeerX - Document Details (Isaac Councill, Lee Giles): Genetic Algorithms are adaptative procedures of optimization and search inspired in the mechanisms of natural selection and genetic. Currently, these algorithms are being highly considered above all in those problems with ...
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