Article
Computer Science, Information Systems
Fanyong Meng, Shyi-Ming Chen, Jie Tang
Summary: This paper introduces a method to address multicriteria decision making problems with interaction criteria, proposing new concepts such as reverse Choquet integral and bi-direction Choquet integral. Through the proposed new Choquet integrals, the paper effectively deals with MCDM problems.
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
Computer Science, Artificial Intelligence
Lesheng Jin, Radko Mesiar, Ronald R. Yager
Summary: This article introduces the concept of derived fuzzy measures for generalizing existing fuzzy measures. Detailed methods for defining corresponding derived Choquet integrals based on derived fuzzy measures are presented. The study shows that both Choquet and Sugeno integrals can be regarded as special cases of derived Choquet integrals.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Mathematical & Computational Biology
Malgorzata Plechawska-Wojcik, Pawel Karczmarek, Pawel Krukow, Monika Kaczorowska, Mikhail Tokovarov, Kamil Jonak
Summary: This study focused on verifying suitable aggregation operators to accurately differentiate neurophysiological features extracted from EEG recordings of schizophrenia patients and healthy controls. The results showed that using the extended versions of the Choquet integral can improve classification accuracy.
FRONTIERS IN NEUROINFORMATICS
(2021)
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, Theory & Methods
Jingqian Wang, Xiaohong Zhang, Jianhua Dai, Jianming Zhan
Summary: This paper proposes a method based on TI-fuzzy fl-neighborhood measures to deal with granularity reduction and decision making in a fuzzy fl-covering approximation space. Four pairs of TI-fuzzy fl-neighborhood measures are presented to replace rough approximation operators in granular computing. A novel method using TI-fuzzy fl-neighborhood measures is introduced for granularity reduction. Four pairs of generalized Choquet integrals based on the TI-fuzzy fl-neighborhood measures are constructed. The proposed methods are illustrated through numerical examples and UCI datasets.
FUZZY SETS AND SYSTEMS
(2023)
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
Gleb Beliakov, Dmitriy Divakov
Summary: This paper outlines recent trends in capacity-based aggregation in large universes, discussing the importance of fuzzy measures in modeling input dependencies and exploring the challenges and approaches to reducing the complexity of aggregation.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Gleb Beliakov, Jian-Zhang Wu
Summary: This study discusses the role of fuzzy measures in aggregation problems and the challenges of obtaining fuzzy measure coefficients from domain experts or empirical data. It introduces the concepts of k-additivity and k-maxitivity to simplify fuzzy measures, and explores the importance of learning fuzzy measures from data.
FUZZY SETS AND SYSTEMS
(2021)
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, Theory & Methods
L'ubomira Horanska, Zdenko Takac
Summary: The aim of this paper is to study comonotone k-maxitive aggregation functions and clarify their relations with Sugeno integrals and k-maxitive fuzzy measures. A construction method based on a subset of the unit hypercube that fully determines comonotone k-maxitive aggregation functions is proposed.
FUZZY SETS AND SYSTEMS
(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, Theory & Methods
Gleb Beliakov, Marek Gagolewski, Simon James
Summary: The use of the Choquet integral allows for effective modeling of interactions and dependencies between data features or criteria in data fusion processes. This paper studies hierarchical aggregation processes that are structurally similar to feed-forward neural networks, simplifying the fitting problem and proposing a fuzzy measure method based on the Mobius representation.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
LeSheng Jin, Anna Kolesarova, Radko Mesiar
Summary: The notion of weak universal integral is defined based on a semicopula, with two particular classes discussed that generalize the Sugeno and Shilkret integrals. In special cases where semicopulas are bounded by the Lukasiewicz t-norm, the introduced integrals reduce to corresponding smallest semicopula based universal integrals. Notably, the proposed integrals that generalize the Shilkret integral belong to the class of aggregation functions when considering the product semicopula, unlike the minimum semicopula which generalizes the Sugeno integral.
FUZZY SETS AND SYSTEMS
(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
Gleb Beliakov, Jian-Zhang Wu
Summary: This study discusses the role of fuzzy measures in aggregation problems and the challenges of obtaining fuzzy measure coefficients from domain experts or empirical data. It introduces the concepts of k-additivity and k-maxitivity to simplify fuzzy measures, and explores the importance of learning fuzzy measures from data.
FUZZY SETS AND SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Gleb Beliakov, Francisco Javier Cabrerizo, Enrique Herrera-Viedma, Jian-Zhang Wu
Summary: The theory of capacities provides a powerful formal methodology for addressing criteria dependencies in multiple criteria decision problems. This study focuses on randomly generating capacities for simulation studies and for capacity learning through evolutionary algorithms. The results are supported by extensive numerical evidence and offer a useful tool for large scale simulations.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Tim Wilkin, Gleb Beliakov
Summary: This article discusses techniques for calculating the mode of compositional data and the challenges it faces in real-world applications, such as computational complexity and oversmoothing.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Gleb Beliakov, Marek Gagolewski, Simon James
Summary: The use of the Choquet integral allows for effective modeling of interactions and dependencies between data features or criteria in data fusion processes. This paper studies hierarchical aggregation processes that are structurally similar to feed-forward neural networks, simplifying the fitting problem and proposing a fuzzy measure method based on the Mobius representation.
FUZZY SETS AND SYSTEMS
(2022)
Correction
Computer Science, Theory & Methods
Gleb Beliakov, Enrique de Amo, Juan Fernandez-Sanchez, Manuel Ubeda-Flores
Summary: This article corrects the formula for the best-possible upper bound on the set of copulas with a given value of the Spearman's footrule coefficient that was recently published in [1].
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Information Systems
Gleb Beliakov
Summary: This paper formalizes operations on capacities in matrix algebra framework, expressing various quantities characterizing input importance and dependencies through capacity derivatives. New formulas for Shapley values and nonmodularity indices are found, and relationships between Shapley interaction indices and lower order derivatives at the top and bottom elements of power sets are discovered.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Gleb Beliakov, Dmitriy Divakov
Summary: This paper outlines recent trends in capacity-based aggregation in large universes, discussing the importance of fuzzy measures in modeling input dependencies and exploring the challenges and approaches to reducing the complexity of aggregation.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Gleb Beliakov, Simon James
Summary: This paper discusses the importance of measures of diversity, spread and inequality in various fields, introduces the P-D principle and ordered weighted averaging operators, and proposes the Choquet integral as a potential method for defining welfare measures. The concept of buoyancy is extended to fuzzy measures, and the optimization problem of the Choquet integral under linear constraints is explored, providing an efficient linear programming solution for antibuoyant fuzzy measures.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Gleb Beliakov, Enrique de Amo, Juan Fernandez-Sanchez, Manuel Ubeda-Flores
Summary: This paper investigates the pointwise best-possible bounds on the set of copulas with a given value of the Spearman's footrule coefficient. It is found that the lower bound is always a copula but the upper bound can be a copula or a proper quasi-copula, with both cases being characterized.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Gleb Beliakov
Summary: This article addresses the challenging problem of random sampling from discrete fuzzy measures. It converts the problem to sampling from an order polytope and efficiently handles it using the Markov chain random walk.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Software Engineering
D. Divakov, A. A. Tyutyunnik
Summary: This paper discusses a homogeneous system of functional equations that arises when solving a spectral problem related to guided modes of irregular waveguides. A symbolic method for solving such systems of functional equations is proposed and implemented in the Maple computer algebra system. Numerical computations are conducted to demonstrate the effectiveness of the developed symbolic-numerical method compared to purely numerical techniques.
PROGRAMMING AND COMPUTER SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Gleb Beliakov, Thang Cao, Vicky Mak-Hau
Summary: This study addresses the problems of mutual dependence in multiple criteria decision-making and multiobjective optimization in the context of land combat vehicle selection for Australian Defence. The criteria dependencies are modeled and formalized using fuzzy measure theory. The main challenge lies in the large number of parameters quantifying all criteria interactions. Strategies such as simplifying model construction, eliciting preferences from numerical simulations and decision makers, and translating preferences into a fuzzy measure learning problem are developed. A mathematical programming problem is formulated and tested to determine fuzzy measure parameters for a relatively large number of decision criteria.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Juan Zhao, Tianrui Zong, Yong Xiang, Guang Hua, Xinyu Lei, Longxiang Gao, Gleb Beliakov
Summary: FSMP is a frequency spectrum modification process proposed to defend against collusion attacks, significantly degrading the perceptual quality of colluded files and deterring attackers. The method is orthogonal to traditional anti-collusion methods and can provide double-layer protection.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Software Engineering
D. Divakov, A. A. Tiutiunnik
Summary: This paper investigates the symbolic representation problem for general solutions of systems of ODEs with symbolically defined constant coefficients, and points out that standard computer algebra procedures may overlook the diversity of symbolic representations of eigenvectors. The proposed algorithm aims to find various symbolic representations of eigenvectors for symbolically defined matrices.
PROGRAMMING AND COMPUTER SOFTWARE
(2021)
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)
