Article
Computer Science, Information Systems
Deli Zhang, Radko Mesiar, Endre Pap
Summary: In this study, two generalization types of Choquet integrals are presented. First, a generalized Choquet type integral of a single-valued function is introduced with respect to a set-function and measure. Several of its properties, such as convergence theorems and Jensen's inequality, are proved. Second, in the spirit of the single-valued Choquet integral, a generalized Choquet type set-valued integral for a single-valued function with respect to a set-multifunction and measure is introduced using Aumann integrals as well as various properties, including convergence theorems.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Mikel Uriz, Daniel Paternain, Humberto Bustince, Mikel Galar
Summary: Fuzzy measure-based aggregations consider interactions among input source coalitions, but defining the fuzzy measure is a challenge. This paper proposes a new algorithm for learning fuzzy measure that can optimize any cost function, using advancements from deep learning frameworks. Experimental study with 58 datasets shows the effectiveness of the proposed method in optimizing cross-entropy cost for binary and multi-class classification problems, compared to other state-of-the-art methods for fuzzy measure learning.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Michal Boczek, Lenka Halcinova, Ondrej Hutnik, Marek Kaluszka
Summary: This paper defines a novel survival function based on conditional aggregation operators, which generalizes existing integrals, and introduces the Choquet-Stieltjes functional as well as the conditions under which it can be called an integral.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Gleb Beliakov, Simon James
Summary: This work focuses on solving optimization problems using the Choquet integral as the objective function, which allows for interaction between coalitions of decision variables. Efficient solution approaches are proposed for problems with a large number of variables, leveraging the antibuoyancy property and extending it to general fuzzy measures. Theoretical results are supported by numerical experiments, showing significant performance gains and scalability to a higher number of variables.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Analytical
Monika Kaczorowska, Pawel Karczmarek, Malgorzata Plechawska-Wojcik, Mikhail Tokovarov
Summary: This study investigates the impact of different aggregation functions on the quality of cognitive workload estimation, highlighting the importance of aggregation methods in improving classification results. The combination of classic machine learning models and aggregation methods is proposed as a means to achieve high-quality cognitive workload level recognition while maintaining low computational cost.
Article
Computer Science, Theory & Methods
Deli Zhang, Radko Mesiar, Endre Pap
Summary: This paper generalizes the Choquet integral and obtains various types of nonadditive integrals. The paper also proves related theorems and inequalities.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yasuo Narukawa, Vicenc Torra
Summary: This article proposes a general framework based on fuzzy integrals for score functions of hesitant fuzzy sets, which is applicable to both typical and non-typical hesitant fuzzy sets. Score functions can transform a set of membership degrees into a single membership value, enabling the ranking of alternative options.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Deli Zhang, Radko Mesiar, Endre Pap
Summary: This paper shows that the Jensen's inequality for Choquet integral given by R. Wang ten years ago is incorrect, and introduces modified Jensen's and reverse Jensen's inequalities for Choquet integral. Furthermore, the Jensen's inequality for generalized Choquet integral, recently obtained by the authors, is also modified accordingly.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Bin Pang
Summary: This article continues the theoretical research on overlap functions. Firstly, O-inclusion subsethoods are defined to describe the inclusion degrees between fuzzy sets. Secondly, using O-inclusion subsethoods, the concepts of O-convexities, algebraic O-closure operators, and O-hull operators are proposed and shown to have one-to-one correspondence. Finally, the relationship between O-convexities and fuzzy interval operators is established. These results not only provide a new perspective for theoretical research on overlap functions but also offer a new approach to fuzzifications of convexities.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Xiaohong Zhang, Mei Wang, Benjamin Bedregal, Mengyuan Li, Rong Liang
Summary: This study relaxes the definition of general overlap functions and introduces semi-overlap functions. It explores their algebraic properties and residual implications, and considers the application of QIP in fuzzy modus ponens problems. A new classification algorithm based on semi-overlap functions and QIP is proposed, and its performance is compared with another algorithm.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Applied
Xiaohong Zhang, Rong Liang, Humberto Bustince, Benjamin Bedregal, Javier Fernandez, Mengyuan Li, Qiqi Ou
Summary: This paper introduces the concepts of pseudo overlap functions and pseudo grouping functions, which expand the original notions of overlap and grouping functions. By removing the commutativity of the original functions, the pseudo functions show greater flexibility and practicality, allowing for more in-depth research and applications in various fields.
Article
Multidisciplinary Sciences
Xiaofeng Wen, Xiaohong Zhang, Tao Lei
Summary: In this paper, the concept of intuitionistic fuzzy overlap function is proposed for the first time, and its generating method, representable and unrepresentable examples are given. A new class of intuitionistic fuzzy rough set model is established using the IF-overlap function, leading to an improved TOPSIS method. The flexibility and effectiveness of the new method are demonstrated through comparative analysis.
Article
Computer Science, Artificial Intelligence
Xiang Jia, Yingming Wang
Summary: This paper improves the traditional MCDM techniques by introducing the Choquet integral-based intuitionistic fuzzy arithmetic aggregation (CIIFAA) operator and the Choquet integral-based intuitionistic fuzzy hybrid arithmetic aggregation (CIIFHAA) operator, to better handle decision-making problems in an intuitionistic fuzzy environment.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
H. Bustince, R. Mesiar, J. Fernandez, M. Galar, D. Paternain, A. Altalhi, G. P. Dimuro, B. Bedregal, Z. Takac
Summary: The paper introduces a new class of functions called d-Choquet integrals, which are a generalization of the standard Choquet integral by replacing the difference in the definition with a dissimilarity function. Some d-Choquet integrals are aggregation functions, while others are not, and the conditions for this are explored in the study of their properties.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Yifan Zhao, Kai Li
Summary: This paper introduces two new methods of constructing ordinal sums of fuzzy implications and explores the intersections of a new family of fuzzy implications with other implications. The research shows that this new family of fuzzy implications is different from the others and provides the necessary and sufficient conditions for their distributivity under specific conditions.
FUZZY SETS AND SYSTEMS
(2022)
Article
Mathematics
Xiaohong Zhang, Rong Liang, Benjamin Bedregal
Summary: After investigating naBL-algebras using non-associative t-norms and overlap functions, the study also extended to inflationary BL-algebras as a recent non-associative generalization of BL-algebras, which can be obtained using general overlap functions. In this paper, it is demonstrated through a counterexample that not every inflationary general overlap function can induce an inflationary BL-algebra, leading to the introduction of the concept of weak inflationary BL-algebras. It is then proven that each inflationary general overlap function corresponds to a weak inflationary BL-algebra, allowing a correction of two mistaken results from a previous paper. Additionally, the properties of weak inflationary BL-algebras are discussed, as well as the relationships among various non-classical logic algebras. Furthermore, the theory of filters and quotient algebras of inflationary general residuated lattices (IGRL) and inflationary pseudo-general residuated lattices (IPGRL) is established, along with the characterization of certain types of IGRLs and IPGRLs using naBL-filters, (weak) inflationary BL-filters, and weak inflationary pseudo-BL-filters.
Article
Computer Science, Artificial Intelligence
Nicolas Zumelzu, Benjamin Bedregal, Edmundo Mansilla, Humberto Bustince, Roberto Diaz
Summary: This article introduces the concept of admissible order for fuzzy numbers and proposes a method to construct admissible orders. With this method, the path costs in fuzzy weighted graphs can be ranked.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jonata Wieczynski, Giancarlo Lucca, Gracaliz P. Dimuro, Eduardo N. Borges, Jose A. Sanz, Tiago da Cruz Asmus, Javier Fernandez, Humberto Bustince
Summary: The Choquet integral (CI) is an averaging aggregation function used in fuzzy reasoning method (FRM) and multicriteria decision making to consider the interactions among data/criteria. Various generalizations of CI have been proposed, including C-F-integrals, which offer nonaveraging behavior depending on the function F adopted and show competitive results in classification. This article introduces the concept of dC(F)-integrals as a generalization of C-F-integrals by restricted dissimilarity functions (RDFs), and analyzes their efficiency compared to the standard C-F-integrals.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Mathematics, Applied
Benjamin Bedregal, Lucelia Lima, Marcus Rocha, Gracaliz Dimuro, Humberto Bustince
Summary: This paper focuses on the study of interval-valued Atanassov intuitionistic t-norms and t-conorms, examining important properties and characterizations of certain sub-classes. The paper also considers admissible orders in addition to the usual order on IVAIFS. Furthermore, the paper establishes the foundation for the use of this study in the context of approximate reasoning.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Mathematics, Applied
Jocivania Pinheiro, Helida Santos, Gracaliz P. Dimuro, Benjamin Bedregal, Regivan H. N. Santiago, Javier Fernandez, Humberto Bustince
Summary: This paper discusses the techniques for generating fuzzy implication functions using general overlap/grouping functions instead of traditional associative aggregators and fuzzy negations. The use of general overlap functions allows for a more flexible and widely applicable approach to obtaining implication functions.
Article
Computer Science, Artificial Intelligence
Antonio Francisco Roldan Lopez de Hierro, Concepcion Roldan, Miguel Angel Tiscar, Zdenko Takac, Regivan H. N. Santiago, Gracaliz P. Dimuro, Javier Fernandez, Humberto Bustince
Summary: Automatic image detection is crucial in various real-world scenarios, and overlap indices provide an important tool for comparing fuzzy objects. These indices have been successfully applied in fields such as image processing and decision making. This article introduces the concept of type-(2, k) overlap index in the context of type-2 fuzzy sets and explores its relationships with algebraic structures. The usage of type-(2, k) overlap indices in fuzzy rule-based systems involving type-2 fuzzy sets is also illustrated.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Review
Mathematics, Applied
Miqueias Amorim, Gracaliz Dimuro, Eduardo Borges, Bruno L. Dalmazo, Cedric Marco-Detchart, Giancarlo Lucca, Humberto Bustince
Summary: Edge detection is a crucial process in computer vision with increasing importance in various applications. Determining the edge of objects accurately is challenging due to the uncertainty and vagueness associated with image characteristics. This paper aims to gather and synthesize the current state of the art in image discontinuity detection through a systematic review of the literature. It identifies three approaches: multiple descriptor extraction and data aggregation, aggregation of distance functions and fuzzy C-means, and fuzzy theory such as type-2 fuzzy and neutrosophic sets. This review provides valuable insights and potential directions for future research.
Article
Automation & Control Systems
Alex Bertei, Luciana Foss, Benjamin Bedregal, Renata Reiser
Summary: This paper presents the axiomatic definition of n-dimensional generalized fuzzy correlation coefficient and studies the properties of general overlap functions and fuzzy negations. It provides new methods for analyzing the n-dimensional generalized fuzzy correlation coefficient and explores its applications in solving multi-criteria and decision-making problems based on fuzzy logic extensions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Mikel Ferrero-Jaurrieta, Zdenko Takac, Javier Fernandez, Lubomira Horanska, Gracaliz Pereira Dimuro, Susana Montes, Irene Diaz, Humberto Bustince
Summary: In this article, the authors generalize the Choquet integral to handle vector inputs and combine it with LSTM to create the VCI-LSTM architecture, which is then applied to sequential image classification and text classification problems.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
B. Bedregal, C. G. da Costa, E. Palmeira, E. Mansilla, B. L. L. Bedregal
Summary: Based on an extension of Riemann sums, this paper introduces a new integral notion that can be applied to continuous inclusion functions with non-monotonic intervals. It further explores the applications of this new integral notion in defining interval probability density functions and interval probability distribution functions.
INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
(2023)
Article
Automation & Control Systems
Javier Fumanal-Idocin, Oscar Cordon, Gracaliz Pereira Dimuro, Antonio-Francisco Roldan Lopez-de-Hierro, Humberto Bustince
Summary: Social network analysis is a popular tool for studying relationships between interacting agents. However, it often overlooks domain-specific knowledge and its propagation. In this work, an extension of classical social network analysis is developed to incorporate external information. A new centrality measure, semantic value, and a new affinity function, semantic affinity, are proposed to establish fuzzy-like relationships among actors in the network. The method is applied to analyze different mythologies and compared with existing measures, showing more meaningful results.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jonata Wieczynski, Giancarlo Lucca, Eduardo Borges, Gracaliz Dimuro
Summary: Fuzzy Rule-Based Classification System (FRBCS) is a technique to handle classification problems, and recent studies have explored the use of the Choquet integral and Sugeno integral to improve the system's quality. This study applies the Sugeno integral in the FRM of a widely used FRBCS and analyzes its performance on 33 different datasets. The results are compared to past studies using different aggregation functions, demonstrating the effectiveness of this new approach. A statistical analysis is also conducted.
INTELLIGENT SYSTEMS, PT I
(2022)
Proceedings Paper
Computer Science, Information Systems
Jonata Wieczynski, Giancarlo Lucca, Eduardo Borges, Gracaliz Dimuro, Rodolfo Lourenzutti, Humberto Bustince
Summary: This study introduces a new multi-criteria decision making method called GMC-RTOPSIS, which optimizes the decision-making process by applying CC-integrals. The analysis of the results shows that this method provides more flexibility and certainty in the decision-making process.
ENTERPRISE INFORMATION SYSTEMS, ICEIS 2021
(2022)
Article
Mathematics, Applied
Rui Eduardo Brasileiro Paiva, Benjamin Rene Callejas Bedregal
Summary: This paper discusses the concepts of general quasi-overlaps and general pseudo-overlap functions on bounded lattices, and introduces methods for constructing these functions. By using the notions of pseudo-t-norms and pseudo-t-conorms, the concepts of additive and multiplicative generators for general pseudo-quasi-overlap functions on lattices are generalized, and some related properties are explored.
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
