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Multi-criteria decision making and pattern recognition based on similarity measures for Fermatean fuzzy sets

JOURNAL OF intelligent & FUZZY SYSTEMS 2021年第6期41卷 5847-5863页

作者： Xu, Changlin Shen, Juhong North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc Ning Yinchuan Ningxia Peoples R China

Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. Fermatean fuzzy set proposed by Senapati and Yager, which can easily process uncertain information in decision making, pattern recognition, medical diagnosis et al., is extension of intuitionistic fuzzy set and Pythagorean fuzzy set by relaxing the restraint conditions of the support for degrees and support against degrees. In this paper, we focus on the similarity measures of Fermatean fuzzy sets. The definitions of the Fermatean fuzzy sets similarity measures and its weighted similarity measures on discrete and continuous universes are given in turn. Then, the basic properties of the presented similarity measures are discussed. Afterward, a decision-making process under the Fermatean fuzzy environment based on TOPSIS method is established, and a new method based on the proposed Fermatean fuzzy sets similarity measures is designed to solve the problems of medical diagnosis. Ultimately, an interpretative multi-criteria decision making example and two medical diagnosis examples are provided to demonstrate the viability and effectiveness of the proposed method. Through comparing the different methods in the multi-criteria decision making and the medical diagnosis application, it is found that the new method is as efficient as the other methods. These results illustrate that the proposed method is practical in dealing with the decision making problems and medical diagnosis problems.

关键词： Similarity measure Fermatean fuzzy set Multi-criteria decision making TOPSIS method Medical diagnosis

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Modifying FCM with Grids and Density Peaks 19

Modifying FCM with Grids and Density Peaks

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International Conference on Pattern Recognition and Artificial Intelligence (prAI)

作者： Wan, Renxia Wang, Weiqi North Minzu Univ Ningxia Key Lab Intelligent Informat & Big Data P Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9781450372312

In this paper, we modify the FCM algorithm with grids and density peaks. We apply the density peak clustering algorithm mixed with grid technology to determine the initial clustering positions of FCM. We also use cluster cores instead of cluster centroids, so that the modified algorithm can effectively discover arbitrary shape clusters. Experimental results show that the performance of the proposed algorithm is found to be superior to its ECTs.

关键词： fuzzy C-means density peak cluster core clustering grid

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Robust Fisher-regularized extreme learning machine with asymmetric Welsch-induced loss function for classification

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APPLIED INTELLIGENCE 2024年第13-14期54卷 7352-7376页

作者： Xue, Zhenxia Zhao, Chongning Wei, Shuqing Ma, Jun Lin, Shouhe North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc NingX Yinchuan 750021 Ningxia Peoples R China

In general, it is a worth challenging problem to build a robust classifier for data sets with noises or outliers. Establishing a robust classifier is a more difficult problem for datasets with asymmetric noise distribution. The Fisher-regularized extreme learning machine (Fisher-ELM) considers the statistical knowledge of the data, however, it ignores the impact of noises or outliers. In this paper, to reduce the negative influence of noises or outliers, we first put forward a novel asymmetric Welsch loss function named AW-loss based on asymmetric L2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_data$$\end{document}-loss function and Welsch loss function. Based on the AW-loss function, we then present a new robust Fisher-ELM called AWFisher-ELM. The proposed AWFisher-ELM not only takes into account the statistical information of the data, but also considers the impact of asymmetric distribution noises. We utilize concave-convex procedure (CCCP) and dual method to solve the non-convexity of the proposed AWFisher-ELM. Simultaneously, an algorithm for AWFisher-ELM is given and a theorem about the convergence of the algorithm is proved. To validate the effectiveness of our algorithm, we compare our AWFisher-ELM with the other state-of-the-art methods on artificial data sets, UCI data sets, NDC large data sets and image data sets by setting different ratios of noises. The experimental results are as follows, the accuracy of AWFisher-ELM is the highest in the artificial data sets, reaching 98.9%. For the large-scale NDC data sets and the image data sets, the accuracy of AWFisher-ELM is also the highest. For the ten UCI data sets, the accuracy and F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackag

关键词： Welsch loss Asymmetric loss Fisher-regularized ELM CCCP

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Robust projection twin extreme learning machines with capped L1-norm distance metric

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NEUROCOMPUTING 2023年 517卷 229-242页

作者： Yang, Yang Xue, Zhenxia Ma, Jun Chang, Xia North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc NingX Yinchuan 750021 Ningxia Peoples R China

In this paper, we incorporate the idea of projection twin support vector machines (PTSVM) into the basic framework of twin extreme learning machines (TELM) and first propose a novel binary classifier named projection twin extreme learning machines (PTELM). PTELM is to seek two projection directions in the TELM feature space, such that the projected samples of one class are well separated from those of the other class. Compared with the PTSVM, PTELM tackles nonlinear cases without using several fixed kernel functions, thus PTELM is less sensitive to use specified parameters and can get better classification accu-racy. Then, a new capped L1-norm PTELM (CL1-PTELM) is proposed by introducing capped L1-norm dis-tance metric in PTELM to reduce the effect of outliers. CL1-PTELM overcomes the disadvantages of L2 -norm distance metric and hinge loss. Thus, CL1-PTELM enhances the robust performance of our PTELM. Finally, two effective algorithms are designed to solve the problem of PTELM and to deal with the chal-lenge of CL1-PTELM brought by non-convex optimization problem, respectively. Simultaneously, we the-oretically prove the convergence and local optimality of CL1-PTELM algorithm. Numerical experiments on three synthetic datasets and several UCI datasets show the feasibility and effectiveness of our proposed methods.(c) 2022 Published by Elsevier B.V.

关键词： projection twin support vector machines Twin extreme learning machines CappedL1-norm Robustness Outliers

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Cognitive excursion analysis of uncertainty concepts based on cloud model

COGNITIVE COMPUTATION AND SYSTEMS

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COGNITIVE COMPUTATION AND SYSTEMS 2022年第4期4卷 362-377页

作者： Lin, Xu C. North Minzu Univ Sch Math & Informat Sci Yinchuan Ningxia Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc Ning Yinchuan Ningxia Peoples R China

Based on the characteristics of human cognition and the cloud model, the excursion of uncertain concepts by the similarity between uncertain concepts from the perspective of conceptual cognition is studied. Firstly, considering the different meanings of the numerical characters in cloud concept, the properties of cloud concepts similarity are given. Furthermore, in order to reflect the various similarities that may exist in uncertain concept, five similarities between cloud concepts are constructed, specifically include the similarity between concept expectations, the similarity between concept entropy, the similarity between concept hyper entropy, the shape similarity and the overall similarity. Secondly, the rationality of these proposed similarity measurements is illustrated by comparing with the existing methods through the specific data analysis. Finally, two cognitive experiments, including concept cognitive processes without prior knowledge and concept cognitive processes with prior knowledge (including positive prior knowledge and negative prior knowledge), are designed. These experiments are all used to study the excursion in the process of concept cognition by the similarity of cloud concepts. The experiment results show that the proposed similarities are reasonable by comparing the proposed method with the existing ones, and the effectiveness of the proposed method is also verified by the excursion of concept cognition.

关键词： cloud model cognitive computing concept cognition concept excursion similarity

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Bidirectional Cognitive Computing Model for Uncertain Concepts

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COGNITIVE COMPUTATION 2019年第5期11卷 613-629页

作者： Xu, Changlin Wang, Guoyin North Minzu Univ Sch Math & Informat Sci Ningxia 750021 Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc Ning Ningxia 750021 Peoples R China North Minzu Univ Hlth Big Data Res Inst Ningxia 750021 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

Most intelligent computing models are inspired by various human/natural/social intelligence mechanisms during the past 60 years. Achievements of cognitive science could give much inspiration to artificial intelligence. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind's capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. The human cognition has been researched in many fields. Some uncertain theories are briefly analyzed from the perspective of cognition based on concepts. In classical intelligent information systems, original data are collected from environment at first;usually, useful information is extracted through analyzing the input data then, it is used to solve some problem at last. There is a common characteristic between traditional machine learning, data mining, and knowledge discovery models. That is, knowledge is always transformation from data. From the point of view of granular computing, it is a unidirectional transformation from finer granularity to coarser granularity. Inspired by human's granular thinking and the cognition law of "global precedence", the human cognition process is from coarser granularity to finer granularity. Generally speaking, concepts (information and knowledge) in a higher granularity layer would be more uncertain than the ones in a lower granularity layer. A concept in a higher granularity layer would be the abstraction of some objects (data or concepts in a lower granularity layer). Obviously, there is a contradiction between the unidirectional transformation mechanism "from finer granularity to coarser granularity" of traditional intelligent information systems with the global precedence law of human cognition. That is, the human cognition are different the computer cognition for uncertain concept. The human cognition for knowledge (

关键词： Bidirectional cognitive computing model Cognitive computing Cloud model

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An unsupervised user identification algorithm using network embedding and scalable nearest neighbour

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4期22卷 S8677-S8687页

作者： Zhou, Xiaoping Liang, Xun Zhao, Jichao Zhiyuli, Aakas Zhang, Haiyan Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing Peoples R China Renmin Univ China Sch Informat Beijing Peoples R China Ningxia Univ Coll Informat Engn Ningxia Peoples R China

Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users' content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users.

关键词： User identification Social network Unsupervised Network embedding Scalable nearest neighbour Social network integration

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Elliptic shape prior for object 2D-3D pose estimation using circular feature

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EURASIP JOURNAL ON ADVANCES IN SIGNAL prOCESSING 2020年第1期2020卷 1-19页

作者： Li, Cui Chen, Derong Gong, Jiulu Wu, Yangyu Beijing Inst Technol Natl Lab Mechatron & Control Beijing 100081 Peoples R China North Minzu Univ Key Lab Intelligent Informat & Big Data Proc Ning Yinchuan 750021 Ningxia Peoples R China

Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding to it is analyzed to yield a 2D elliptic shape prior. The method employs the combination of the grayscale histogram, the intensities of edge, and the smoothness distribution as main image feature descriptors that define the image statistical measure model. The elliptic shape prior combined with the image statistical measure energy model drives the elliptic shape contour to the projection of the circular feature of the 3D object with the current pose into the image plane. These works effectively reduce the impacts of the challenging scenarios on the pose estimate results. In addition, the method utilizes particle filters that take into account the motion dynamics of the object among scene frames, and this work provides the robust method for object 2D-3D pose estimation using circular feature in a challenging environment. Various numerical experiments are illustrated to show the performance and advantages of the proposed method.

关键词： Pose estimation Circular feature Elliptic shape prior Image statistical property

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Exploration of polygons in online social networks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S3841-S3848页

作者： Zhou, Xiaoping Liang, Xun Zhao, Jichao Zhiyuli, Aakas Zhang, Haiyan Renmin Univ China Sch Informat Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing Peoples R China Ningxia Univ Coll Informat Engn Yinchuan Ningxia Peoples R China

Online social networks have continued to attract increased attention since the introduction of this concept nearly three decades ago. Consequently, a study about the workings of online social networks may help in understanding the structure of human society and the characteristics of generic complex networks. Over the past few years, interest in neighboring nodes, which are the number of nodes between any two nodes, the number of neighbors and how many triangles are present in a social network have produced the concepts of a six-degree separation (Milgram, Psychol Today 2(1):60-67, 1967;Backstrom et al., proceedings of the 4th annual ACM web science conference, p 33-42, 2012), a heavy tail in the degree distribution (Barabasi, Albert, Science 286(5439):509-512, 1999) and a high clustering coefficient (Luce, Perry, Psychometrika 14(2):95-116, 1949;Watts, Strogatz, Nature 393(6684):440-442, 1998;Amaral et al., proc Natl Acad Sci USA 97(21):11149-11152, 2000). In a similar manner, researchers have also been curious about how many polygons are present in a given online social network. Although much effort has been expended (Dantzig et al., International symposium on theory of graphs, p 77-83, 1967;Kamae, IEEE Trans Circuit Theory 14(2):166-171, 1967;Gotlieb, Corneil, Commun ACM 10(12):780-783, 1967;Welch, J ACM 13(2):205-210, 1966;Tiernan, Commun ACM 13(12):722-726, 1970;Tarjan, SIAM J Comput 2(3):211-216, 1973;Johnson, SIAM J Comput 4:77-84, 1975;Mateti, Deo, SIAM J Comput 5(5):90-99, 1976;Marinari et al., Europhys Lett 73(3):301-307, 2005), studying this subject, the inability to enumerate polygons has stymied an in depth understanding of the properties of polygons in an online social network. In the study described in this paper, the estimated number of polygons in an online social network is revealed. It was found that in the current widely used online social networks, e.g., Facebook, Twitter, the number of polygons increases drastically when the length of a polygon

关键词： Polygon Social network Polygon distribution Clustering coefficient

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Convergence Rate Analysis of a Class of Uncertain Variational Inequalities

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IEEE ACCESS 2023年 11卷 57772-57782页

作者： Li, Cunlin Zhao, Teng Yee, Hooi Min North Minzu Univ Sch Management Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Key Lab Intelligent Informat & Big Data Pr Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan 750021 Ningxia Peoples R China Univ Teknol MARA Civil Engn Studies Permatang Pauh 13500 Pulau Pinang Malaysia

Variational inequalities theory is a potent tool that can be employed to tackle diverse optimization problems, with applications spanning physics, economics, finance and beyond. Solving the uncertain variational inequalities (UVI) has emerged as a prominent research topic in academia. However, conventional regularization methods for solving UVI are subject to certain limitations on the convergence rate of the algorithm. To overcome this challenge, we opt a class of regularized gap functions which is lower semi-continuous and f -bounded true convex properties. We establish an expected value equivalent optimization model and demonstrate that the exponential convergence of the regularized gap function. Furthermore, we investigate the subdifferential of the lower semi-continuous and f -bounded true convex functions (LSTCOS) and develop a deviation between the LSTCOS and its closed convex hull. By utilizing their deviations, we transform the convergence rate of the UVI solution to the uniform exponential convergence of LSTCOS. Finally, we prove that the UVI solution uniformly converges at an exponential rate based on the generalized equation.

关键词： Uncertain variational inequality f-bounded true convex function exponential convergence

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