Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions

Yuan, Zhong; Chen, Hongmei; Xie, Peng; Zhang, Pengfei; Liu, Jia; Li, Tianrui

doi:10.1016/j.asoc.2021.107353

Cited by 90 publications

(9 citation statements)

References 122 publications

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“…An et al [31] developed a robust fuzzy rough set based on kNN granules and then followed the approximation expression to design a fuzzyrough-uncertainty-based semi-supervised feature selection. More information can be found in [32] that recalled several important foundations related to the model of fuzzy rough theory, and then investigated so many advanced corresponding attribute reduction schemes comprehensively.…”

Section: Related Workmentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature's significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

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Section: Related Workmentioning

confidence: 99%

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Gao

2024

IEEE Access

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“…The innovativeness of componentbased complex products has not been analyzed [14,15]. Optimization of the component product structure has not been explicitly performed [16][17][18][19]. The investment attractiveness of component-based innovative product projects was practically not considered [20][21][22].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

Methods and Models of Research of Investment Attractiveness and Competitiveness of Project-Oriented Enterprise in the Process of Creating Innovative High-Tech

Fedorovich

Кosenko²,

Lutai³

et al. 2022

ITSSI

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The subject of the research is the investment attractiveness of enterprises producing competitive products with modern component architecture. The aim of the article is to develop complex methods for research of investment attractiveness of projects on creation of new high-tech products with component architecture. Tasks to be solved are: to use component method for the analysis of innovativeness of created high-tech products; to study investment attractiveness of an innovation project with the use of expert assessments and the method of planning experiments; to substantiate the composition of a diversification project portfolio for investment taking into account the limited possibilities of the enterprise based on the method of integer optimization; to study the performance of the project portfolio using agent-based imitational modeling. Methods used are: system analysis, component design, multi-criteria optimization, expert assessment and agent-based simulation modeling in the form of applied information technology. The results have been obtained: the analysis of investment attractiveness of projects on creation of new high-tech products with component architecture is carried out. The architecture of a complex product built with components from previous positive development experience is investigated. A classification of possible components in the architecture of a complex product is created. A set of indicators for assessing the investment attractiveness of an innovative component-based product is formed. The qualitative assessments of experts for the main indicators of the component composition of the product in the form of linguistic variables are proposed. The use of multifactor experiment to assess the investment attractiveness of a project to create a complex product with a component-based architecture is proposed. An optimization model for selecting the rational composition of components in an innovative product is developed. An agent-based simulation model is built to study the performance of work on the preparation of production of innovative products on a component basis. Conclusions: The use of a set of developed methods and applied information technology for the creation of high-tech products with component-based architecture allows you to plan a diversification portfolio of projects with investment attractiveness and competitiveness of products.

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“…In the process of constructing the discernibility matrix, the core attributes are obtained by finding the minimum element in the discernibility matrix and identifying the reducts [25]. Heuristic algorithms [26]- [28] typically calculate the attribute dependency to determine the reducts. Examples include test-cost-sensitive attribute reduction [29] and attribute reduction based on the conditional information entropy [30].…”

Section: Introductionmentioning

confidence: 99%

Local Indiscernibility Relation Reduction for Information Tables

Tang

Tan

2022

IEEE Access

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Attribute reduction comes from machine learning and is an important component of rough set theory. Research on attribute reduction has produced many important achievements. The aim of attribute reduction is to reduce the complexity of data while retaining its original characteristics to the greatest extent. The concept of attribute reduction is of great significance in machine learning research. In previous studies, a variety of attribute reduction definitions have been proposed according to different rules. Based on the binary relations among objects and local decision rules, this paper describes a local indiscernibility relation reduction for information tables. The discernibility matrix for the proposed reduction is established, and examples for single-and multi-decision classes are presented to illustrate that the proposed local indiscernibility relation reduction can be applied to decision tables. According to the reduction concept developed in this paper, and considering a heuristic algorithm for calculating the significance of attributes and a binary integer programming algorithm based on the discernibility matrix, three reduction algorithms are proposed. Experiments are conducted using four classifiers and a number of publicly available datasets. A comparison of the experimental results presented in this paper demonstrates the feasibility of the proposed algorithms.

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Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions

Cited by 90 publications

References 122 publications

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Effective Attribute Reduction Algorithm Based on Fuzzy Uncertainties Using Shared Neighborhood Granulation

Methods and Models of Research of Investment Attractiveness and Competitiveness of Project-Oriented Enterprise in the Process of Creating Innovative High-Tech

Local Indiscernibility Relation Reduction for Information Tables

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