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Distance Metric Learning with Prototype Selection for Imbalanced Classification 1

16th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2021

作者： Suárez, Juan Luis García, Salvador Herrera, Francisco Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (数字)9783030862718

ISBN: (纸本)9783030862701

Distance metric learning is a discipline that has recently become popular, due to its ability to significantly improve similarity-based learning methods, such as the nearest neighbors classifier. Most proposals related to this topic focus on standard supervised learning and weak-supervised learning problems. In this paper, we propose a distance metric learning method to handle imbalanced classification via prototype selection. Our method, which we have called condensed neighborhood components analysis (CNCA), is an improvement of the classic neighborhood components analysis, to which foundations of the condensed nearest neighbors undersampling method are added. We show how to implement this algorithm, and provide a Python implementation. We have also evaluated its performance over imbalanced classification problems, resulting in very good performance using several imbalanced score metrics. © 2021, Springer Nature Switzerland AG.

关键词： Python

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Large-scale group decision-making method based on knowledge differentiation and the grouping of alternatives into modifiable scenarios 9th

Large-scale group decision-making method based on knowledge ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute of Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

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Challenges in Fuzzy Decision Making for Future Research 21

Challenges in Fuzzy Decision Making for Future Research

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Trillo, José Ramón Herrera-Viedma, Enrique Higueras-Ruiz, María José Alonso, Sergio Morente-Molinera, Juan Antonio Cabrerizo, Francisco Javier Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain Department of Information and Communication University of Granada Granada18071 Spain Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada18071 Spain

ISBN: (纸本)9781643683164

Fuzzy Decision Making is a line of research that has had an important presence in the literature since 1965 until nowadays. From that year to the present, the line of research has been consolidated. The research field goals have evolved along with all the research that makes use of these systems and the new technologies requirements. Therefore, new challenges, different from the ones that were faced at the beginning, need to be solved. The aim of this paper is to describe the recent challenges faced by those working in this line of research. This paper will help researchers to know how their work should be oriented in the future. © 2022 The authors and IOS Press. All rights reserved.

关键词： Decision making

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Evaluating soil respiration and water infiltration in esparto grasslands: the effects of hillslope position and soil management in arid, human-affected Mediterranean environments

Discover Sustainability

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Discover Sustainability 2025年第1期6卷 1-15页

作者： Arredondo, Alicia M. Mirzaei, Morad Wu, Gao-Lin Keshavarzi, Ali Durán-Zuazo, Víctor Hugo Bernardo, Francisco Serrano Rodrigo-Comino, Jesús Departamento de Análisis Geográfico Regional y Geografía Física Facultad de Filosofía y Letras Campus Universitario de Cartuja Universidad de Granada Granada Spain School of Natural Sciences Botany Discipline Trinity College Dublin Dublin Ireland Environment Soils and Land-Use Department Teagasc Co. Wexford Ireland State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Institute of Soil and Water Conservation Northwest A & F University Yangling China Qinghai Provincial Key Laboratory of Adaptive Management on Alpine Grassland Qinghai Academy of Animal and Veterinary Science Qinghai University Xining China Institute of Soil and Water Conservation Chinese Academy of Sciences and Ministry of Water Resource Yangling China CAS Center for Excellence in Quaternary Science and Global Change Xi’an China Laboratory of Remote Sensing and GIS Department of Soil Science Faculty of Agriculture University College of Agriculture & Natural Resources University of Tehran Karaj Iran Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA) IFAPA Center “Camino de Purchil” Granada Spain Departamento de Ingeniería Civil ETSI Caminos Canales y Puertos Universidad de Granada Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada Spain

Stipa tenacissima L., commonly known as esparto grass, is a key species in semi-arid Mediterranean ecosystems, historically valued for its use in crafts and agriculture. However, the widespread abandonment of esparto grasslands has led to significant soil degradation, including erosion, reduced water retention, and nutrient loss. In Benamaurel, Granada (Southern Spain), where these grasslands once thrived, abandonment has exacerbated soil degradation, driven by the region’s distinct geomorphological and climatic conditions, such as saline soils, gypsum deposits, and extreme temperatures. This study aims to assess soil infiltration and respiration dynamics in both cultivated and abandoned esparto grasslands in Benamaurel, considering different hillslope positions (upper, backslope, and footslope). Our results demonstrate significant variability in soil infiltration (differences between 0.5 and 1.5 mm h−1) and respiration (difference of −9.17 µg m2 hr⁻1 in CO2 emissions), with no consistent trends identified across different hillslope positions or types of land management. Key soil properties, including bulk density, organic matter, and soil water retention capacity (SWRC), play a critical role in these processes, though their effects vary. Long-term monitoring is essential for understanding these dynamics, especially in the context of climate change. Our findings highlight the need for conservation strategies to prevent further soil degradation, promote landscape restoration, and reduce environmental risks. Gaining insight into the effects of abandonment on soil quality in this region is crucial for developing effective land management practices.

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Pattern Mining with Evolutionary Algorithms

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2018年

作者： Sebastin Ventura José María Luna

ISBN: (纸本)9783319816180

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and i

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Supervised Descriptive Pattern Mining

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2018年

作者： Sebastin Ventura José María Luna

ISBN: (纸本)9783319981390

This book provides a general and comprehensible overview ofsupervised descriptive pattern mining, considering classicalgorithms and those based on heuristics. It providessome formal definitions and a general idea about patterns, pattern mining,the usefulness of patterns in the knowledge discovery process, as well as abrief summary on the tasks related to supervised descriptive patternmining. It also includes a detailed description on the tasks usually groupedunder the term supervised descriptive pattern mining: subgroups discovery,contrast sets and emerging patterns. Additionally, this book includes twotasks, class association rules and exceptional models, that are alsoconsidered within this field. A major feature of this book is that it provides a general overview (formaldefinitions and algorithms) of all the tasks included under the termsupervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies forany of these tasks. This book also illustrates how important these techniques arein different fields, a set of real-world applications are described. Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the superviseddescriptive pattern mining field, describing its tasks, its algorithms, its applications,and related tasks (those that share some common features). This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-basedalgorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methodsand tools) as a secondary textbook.

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Feature Importance Identification for Time Series Classifiers

Feature Importance Identification for Time Series Classifier...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Meng, Han Wagner, Christian Triguero, Isaac Lab United Kingdom School of Computer Science Nottingham United Kingdom DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Department of Computer Science and Artificial Intelligence Granada Spain

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Time series classification is a challenging research area where machine learning techniques such as deep learning perform well, yet lack interpretability. Identifying the most important features for such classifiers provides a pathway to improving their interpretability. Several Feature Importance (FI) identification methods remove the contributions of features, i.e. observations at certain time steps of, from the input and evaluate the change in the classification result to measure the importance of features. As time series features cannot simply be deleted, current techniques generally rely on replacing features with constant or random values. While effective, this approach risks unexpected results in the classification and thus feature importance estimation-as the replacements used may be different to what the classifier encountered in the training phase. This is referred to as the Out-Of-Distribution problem. The OOD problem has been recognised in image and language models but have not received much attention in the context of time series classification. This work addresses the OOD problem in FI identification for time series classifiers. Specifically, we propose a method based on Conditional Variational Autoencoder to generate possible sets of within-distribution inputs, which are used to evaluate feature importance through marginalisation. Experiments on publicly accessible datasets are carried out showing that the method identifies the most important features with higher accuracy than existing methods, providing the basis for improved explainability of time series classifiers. © 2022 IEEE.

关键词： Time series

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Fuzzychain: An equitable consensus mechanism for blockchain networks

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Journal of Network and Computer Applications 2025年 241卷

作者： Bruno Ramos-Cruz Javier Andreu-Perez Francisco J. Quesada-Real Luis Martínez Computer Science Department University of Jaen Jaen Jaen 23071 Spain Centre for Computational Intelligencer School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Jaen Jaen 23071 Spain

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain, leverages fuzzy sets to define stake semantics, introducing a degree of softness in validator selection. This approach mitigates rigid threshold-based decision-making by allowing gradual transitions between stake levels, reducing sharp disparities among validators. By incorporating this enhanced stake evaluation, Fuzzychain promotes a more adaptive and distributed selection process, ensuring a fairer and more inclusive blockchain network. A thorough assessment of a real-time multi-agent blockchain system to examine validator selection and reduce inequality, promoting a more equitable distribution of stakes among validators compared to other consensus mechanisms. This fosters a more inclusive selection process and a more equitably distributed network.

关键词： Fuzzychain Fuzzy sets Consensus algorithm Blockchain Distributed networks

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pyDML: a python library for distance metric learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 3808-3814页

作者： Juan Luis Suárez Salvador García Francisco Herrera DaSCI Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain

pyDML is an open-source python library that provides a wide range of distance metric learning algorithms. Distance metric learning can be useful to improve similarity learning algorithms, such as the nearest neighbors classifier, and also has other applications, like dimensionality reduction. The pyDML package currently provides more than 20 algorithms, which can be categorized, according to their purpose, in: dimensionality reduction algorithms, algorithms to improve nearest neighbors or nearest centroids classifiers, information theory based algorithms or kernel based algorithms, among others. In addition, the library also provides some utilities for the visualization of classifier regions, parameter tuning and a stats website with the performance of the implemented algorithms. The package relies on the scipy ecosystem, it is fully compatible with scikit-learn, and is distributed under GPLv3 license. Source code and documentation can be found at https://***/jlsuarezdiaz/pyDML.

关键词： distance metric learning classification mahalanobis distance dimensionality Python

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An Increasing Assessment theof dependability Different Reinforcement of critical safety Learning systems Methods through for Creating aReinforcement Decision Support learning Systemwith Based Petri onnet themodel Petri Net Model

An Increasing Assessment theof dependability Different Reinf...

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2022 Annual Conference of the Prognostics and Health Management Society, PHM 2022

作者： Saleh, Ali Chiachio, Manuel Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada18071 Spain

ISBN: (纸本)9781936263370

Infrastructure safety, operation, and management are directly affected by operation strategy which directly influence the life cycle and have a tremendous impact on operation costs. Management strategies can be branched to unrelated decisions which are typically difficult to be formulated mathematically. Reinforcement Learning serves as an adequate tool to account for unrelated decisions and optimize them in accordance with a final goal. Besides, it allows for a strategy to be flexible by autonomously changing with the variation of the system condition without the need for any user intervention. On the other hand, Petri nets are a suitable tool for maintenance modeling, as they can deal with heterogeneous information, parallel operations, and synchronization, and provide a graphical interpretation for the processes. Also, they allow for formulating RL problems based on their standard components (namely, places and transitions) which makes them suitable for forming a hybrid tool with the RL. In this work, an intelligent tool for modeling and optimizing maintenance has been developed by combining high-level Petri net (HLPN) and Reinforcement Learning (RL). RL actions are modeled by creating a special type of PN transitions, and rewards are given in terms of the running revenues and costs. The method was used to model a safety-critical system (SCS) and optimize its maintenance schedule in order to avoid the catastrophic consequences of failures while reducing the running costs. The results show that the proposed approach is capable of providing an intelligent management tool for SCS operation under flexible, yet unveiled policies, and also allows assessment of the safety of the system based on the simulated system states. © 2022 Prognostics and Health Management Society. All rights reserved.

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