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1. Knowledge Framework and Evolution of Fuzzy Portfolio Research: A Bibliometric Analysis.

Author

Zhou, Wei, Gu, Qinen, and Yu, Dongping

Subjects
SET theory, OPERATIONS research, FUZZY sets, COMPUTER science, INVESTMENTS, SOFTWARE measurement
Abstract

Recently, wide applications of fuzzy set theory have attracted the attention of both researchers and practitioners. Fuzzy portfolio develops as a new area in the research field of investment portfolio. This paper investigates the major research hotspots, development trend, and evolution of fuzzy portfolio, which provides a systematic review of the current fuzzy portfolio literature. CiteSpace, the most commonly used bibliometrics software, is used in this article. According to the 602 articles with 15132 references, several conclusions can be summarized as follows: (1) Fuzzy portfolio becomes increasingly interdisciplinary with the connections among "Computer Science" and so on. (2) Most contributive authors are Markowitz and Zadeh. (3) South China University of Technology makes excellent performance in this research area and China is the most influential country. (4) European Journal of Operations Research is the cradle of plenty of crucial fuzzy portfolio investigations. (5) We find some research hotspots helpful to make scientific predictions of future trends by analyzing the keywords. By utilizing the effective bibliometric methods, we provide a comprehensive analysis and deep insights into the fuzzy portfolio research, enabling the individuals, especially the new beginners who are interested in this area to learn fuzzy portfolio, which will be of great help for their future explorations. [ABSTRACT FROM AUTHOR]

Published
2020
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2. Training Classifiers under Covariate Shift by Constructing the Maximum Consistent Distribution Subset.

Author

Yu, Xu, Yu, Miao, Xu, Li-xun, Yang, Jing, and Xie, Zhi-qiang

Subjects
*ANALYSIS of covariance, *SET theory, *DISTRIBUTION (Probability theory), *ESTIMATION theory, *GENERALIZATION, *COMPUTER science
Abstract

The assumption that the training and testing samples are drawn from the same distribution is violated under covariate shift setting, and most algorithms for the covariate shift setting try to first estimate distributions and then reweight samples based on the distributions estimated. Due to the difficulty of estimating a correct distribution, previous methods can not get good classification performance. In this paper, we firstly present two types of covariate shift problems. Rather than estimating the distributions, we then desire an effective method to select a maximum subset following the target testing distribution based on feature space split from the auxiliary set or the target training set. Finally, we prove that our subset selection method can consistently deal with both scenarios of covariate shift. Experimental results demonstrate that training a classifier with the selected maximum subset exhibits good generalization ability and running efficiency over those of traditional methods under covariate shift setting. [ABSTRACT FROM AUTHOR]

Published
2015
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3. Traffic Multiresolution Modeling and Consistency Analysis of Urban Expressway Based on Asynchronous Integration Strategy

Author

Bin Ran, Lixin Yan, Liyan Zhang, and Jian Ma

Subjects
050210 logistics & transportation, Article Subject, Computer science, 05 social sciences, Detector, General Engineering, Process (computing), Traffic simulation, 010501 environmental sciences, Traffic flow, 01 natural sciences, lcsh:QA75.5-76.95, Computer Science Applications, Consistency (database systems), Asynchronous communication, Modeling and Simulation, 0502 economics and business, lcsh:Electronic computers. Computer science, Set theory, Algorithm, 0105 earth and related environmental sciences, Traffic wave
Abstract

The paper studies multiresolution traffic flow simulation model of urban expressway. Firstly, compared with two-level hybrid model, three-level multiresolution hybrid model has been chosen. Then, multiresolution simulation framework and integration strategies are introduced. Thirdly, the paper proposes an urban expressway multiresolution traffic simulation model by asynchronous integration strategy based on Set Theory, which includes three submodels: macromodel, mesomodel, and micromodel. After that, the applicable conditions and derivation process of the three submodels are discussed in detail. In addition, in order to simulate and evaluate the multiresolution model, “simple simulation scenario” of North-South Elevated Expressway in Shanghai has been established. The simulation results showed the following.(1)Volume-density relationships of three submodels are unanimous with detector data.(2)When traffic density is high, macromodel has a high precision and smaller error and the dispersion of results is smaller. Compared with macromodel, simulation accuracies of micromodel and mesomodel are lower but errors are bigger.(3)Multiresolution model can simulate characteristics of traffic flow, capture traffic wave, and keep the consistency of traffic state transition. Finally, the results showed that the novel multiresolution model can have higher simulation accuracy and it is feasible and effective in the real traffic simulation scenario.

Published
2017

4. Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Author

Romualdas Bausys, Jurgita Antucheviciene, Othman Ibrahim, Madjid Tavana, Mehrbakhsh Nilashi, and Abbas Mardani

Subjects
0209 industrial biotechnology, General Computer Science, Computer science, 02 engineering and technology, computer.software_genre, Machine learning, Fuzzy logic, lcsh:QA75.5-76.95, Field (computer science), 020901 industrial engineering & automation, Knowledge extraction, 0202 electrical engineering, electronic engineering, information engineering, Set theory, Multidisciplinary, business.industry, Intelligent decision support system, Inductive reasoning, Data science, Expert system, Systematic review, 020201 artificial intelligence & image processing, lcsh:Electronic computers. Computer science, Rough set, Artificial intelligence, business, computer
Abstract

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called “PRISMA,” has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

Published
2017