Rough Set Research Articles

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

An Extension of VIKOR Approach for MCDM Using Bipolar Fuzzy Preference δ-Covering Based Bipolar Fuzzy Rough Set Model

Bipolarity highlights the positive and negative facets of a certain dilemma. This script aims to propose a novel multi-criteria decision-making (MCDM) approach based on bipolar fuzzy preference δ-covering based bipolar fuzzy rough set (BFPδC-BFRS) model by combining the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Rasenje) scheme. The VIKOR scheme is viewed as a beneficial MCDM strategy, particularly in situations where an expert is incapable of making the right decision at the beginning of system design. The VIKOR method works well for problems with competing attributes because it operates under the presumptions that compromise is acceptable in conflict resolution, the expert seeks a solution that is extremely close to the best, and all developed attributes are taken into consideration when processing the various alternatives. In this study, firstly, we proposed an integrated MCDM based on BFPδC-BFRSs using the VIKOR methodology. Moreover, we solve a real-world illustration to show the effectiveness of the expanded VIKOR approach. Finally, we demonstrate a detailed comparative analysis of the proposed methodology with some prevalent decision-making approaches to substantiate the accountability of the recommended scheme.

Detection of Assaults in Network Intrusion System using Rough Set and Convolutional Neural Network

Detection of Assaults in Network Intrusion System using Rough Set and Convolutional Neural Network

A novel prediction method for rolling contact fatigue damage of the pearlite rail materials based on shakedown limits and rough set theory with cloud model

Evaluation and prediction of wheel-rail rolling contact fatigue (RCF) damage can provide important theoretical guarantees for the service safety of wheels and rails and help make maintenance easier to plan. This study aims to develop a novel method for evaluating and predicting RCF damage of the pearlite rail materials with various initial shear yield strengths (ke). Based on the rough set mathematical theory incorporated within the cloud model of the comprehensive evaluation index (P0/ke*μt), a novel evaluation and prediction method for RCF damage states of various pearlite rail materials was constructed using the shakedown limits for pearlite rail materials with various initial shear yield strengths. To develop this novel prediction method, different evaluation indices for RCF damage states were designed. A comprehensive certainty approach was introduced to quantitatively analyze the actual measured values of distinct evaluation indices that corresponds to different RCF damage states, wherein the maximum value rule was applied. Moreover, the prediction results were confirmed after further verifying using the actual measured value of the P0/ke*μt. The results indicated that the predicted results were consistent with the test outcomes. The key feature of this prediction method was that it involved both the intrinsic shear yield strength of evaluated pearlite rail materials and wheel-rail rolling contact variables. On the basis of the two-dimensional classical shakedown map, a three-dimensional shakedown limit diagram for rail materials with varying initial shear yield strengths was further constructed using this novel prediction method. The three-dimensional shakedown limit diagram featured an inclined curved surface. As the initial shear yield strength of the pearlite rail materials increased, the curved surface tilted downward, indicating that an increase in the initial ke value of the pearlite rail materials could result in a lower shakedown limit.

Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST

The Fourth Industrial Revolution, also known as Industry 4.0, which is the intensified digitalization and automation in industry, embraces cyber–physical systems, the Internet of Things (IoT), and artificial intelligence, among others. This study utilizes Fuzzy Integration–Rough Set Theory (FI-RST) analysis to quantify the impacts of the imperative Industry 4.0 technologies for manufacturing firms located in Fujian Province, China, namely, Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM), on the sustainable development performance of firms. The findings of the study indicate that these technologies greatly improve the effectiveness of the utilization of resources, reduce the costs of operations, and reduce the impact on the environment. In addition, they have a favorable influence on social considerations, such as preserving the well-being of employees and the outcome of training programs. This research work has convincingly provided an underlying strategic adoption of these technologies for sustainability production by raising important insights that could be valuable for industry managers and policymakers, especially those seeking sustainability at the global level.

An Enhanced Algorithm for Hybrid Flow Shop Scheduling Using Discrete Sparrow Optimization and Rough Set Theory

Aiming at the shortcomings of the sparrow search algorithm, such as it is easy to fall into local optimum and unable to solve discrete optimization problems, an improved discrete sparrow search algorithm is proposed. Firstly, the position update formula of the original sparrow search algorithm is abstracted, a new discrete heuristic position update strategy is designed according to the different identities of individuals, and the encoding and decoding methods are designed for the hybrid flow shop scheduling problem; Secondly, the rough data-deduction theory is introduced, and the feasibility and rationality of the above theory are explained by mathematical proofs, which provides theoretical support for the algorithm and improves the interpretability; Then, the nature of upper approximation is adopted to expand the search space, improve the population diversity, avoid prematurity of the algorithm, combine division and rough data-deduction to propose three strategies to promote information sharing among populations, regulate the exploitation ability and exploration ability of populations, and reduce the probability of the algorithm falling into local optimum; Finally, the improved discrete sparrow search algorithm is used to solve the hybrid flow shop scheduling problem. Simulation experiments are carried out on three small-scale practical examples and Liao's classic test set to verify the feasibility of the improved discrete sparrow search algorithm to solve the hybrid flow shop scheduling problem, and to prove the superiority of the proposed algorithm and the effectiveness of the improved strategy through comparison experiments with other algorithms.

Information entropy-assisted intuitionistic fuzzy rough feature subset selection

Fuzzy-rough set (FRS) has been effectively implemented as a powerful pre-processing tool to cope with the issues such as vagueness, imprecision, noise and uncertainty in high-dimensional data analysis. However, FRS fails to handle the uncertainty due to identification, which is very much common in heterogeneous data. Moreover, it is always challenging to handle different issues such as vagueness, uncertainty, and noise during elimination of irrelevant and redundant features. This paper investigates an intuitionistic fuzzy rough set (IFRS)-aided feature selection method by using information entropy notion to tackle these issues simultaneously. We establish an intuitionistic fuzzy (IF) similarity relation. Then IFRS model is discussed based on this relation. Next, we present a novel IF granular structure. Based on the granular structure, we present lambda-conditional entropy for IF framework. Further, relevant mathematical theorems are proved to justify the theoretical aspect of the models. Moreover, a positive region preserved attribute selection method is proposed. A comprehensive experimental study is discussed to demonstrate the effectiveness and practical validation of the proposed method. Finally, a methodology is developed based on proposed models, which increases accuracy for the minority RNA silencing suppressors against majority class non-suppressors as evident from better sensitivity and G-means metrics.

Attribute reduction based on directional semi-neighborhood rough set

Attribute reduction based on directional semi-neighborhood rough set

Semiconcept and concept representations

In FCA, we often deal with a formal context K=(G,M,I) that is only partially known, i.e. only the attributes that belong to an observable set N⊂M are known. There must also exist a part H of the object set G – called a training set – that consists of elements with all attributes known. The concepts of K have to be determined using the subcontexts corresponding to the training object set H and to the observable attribute set N. In our paper, this problem is examined within the extended framework of the semiconcepts of the original context, which are generalizations of its concepts. Each semiconcept of the original context induces a semiconcept in both subcontexts. In this way, each semiconcept of the context is represented by an induced pair of semiconcepts, which can also be considered its approximations — as in the case of rough sets. We describe the properties of the mapping defined by this representation and prove that the poset formed by these semiconcept pairs is a union of two complete lattices. We show that these induced semiconcept pairs can be generated by using a simplified representation of them. As the number of semiconcepts grows exponentially with the size of the training set and the observable attribute set, an algorithm that selects the representation pairs for which their support and relevance reach a certain threshold is also presented.

A rough set-based model for predicting soil greenhouse gases response to biochar

Biochar application to soil is a potential climate change mitigation strategy. In addition to long-term sequestration of the carbon content of the biochar itself, its application may reduce the emissions of other greenhouse gases (GHGs) from the soil. However, the reported effects of biochar application on soil GHG fluxes exhibit inconsistent results. Prediction of such effects is an important gap that needs to be addressed in biochar research. In this study, rule-based machine learning models were developed based on rough-set theory. Data from the literature were used to generate the rules for predicting the effects of biochar application on soil GHG (CO2, N2O, and CH4) fluxes. Four rule-based models for CO2 fluxes, two rule-based models for N2O fluxes, and three rule-based models for CH4 fluxes were developed. The validity of these models was assessed based on both statistical measures and mechanistic plausibility. The final rule-based models can guide the prediction of changes in soil GHG fluxes due to biochar application, and thus serve as a decision-support tool to maximize the benefits of biochar application as a negative emission technology (NET). In particular, mechanistically plausible rules were identified that predict triggers for GHG fluxes that can offset carbon sequestration gains. This knowledge allows biochar application to be calibrated to local conditions for maximum efficacy.Graphical

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

A 4-valued logic for double Stone algebras

This paper investigates the logical structure of the 4-element chain considered as a double Stone algebra. It has been shown that any element of a double Stone algebra can be identified as monotone ordered triplet of sets. As a consequence, we obtain the 4-valued semantics for the logic LD of double Stone algebras. Furthermore, the rough set semantics of the logic LD is provided by dividing the boundary region (uncertainty) into two disjoint subregions.

Academic resilience of nursing students during COVID-19: An analysis using machine learning methods.

This cross-sectional study investigates the factors that contribute to academic resilience among nursing students during COVID-19 pandemic. A cross-sectional study. A survey was conducted in a general hospital between November and December 2022. The Nursing Student Academic Resilience Inventory (NSARI) model was used to assess the academic resilience of 96 nursing students. The Boruta method was then used to identify the core factors influencing overall academic resilience, and rough set analysis was used to analyse the behavioural patterns associated with these factors. Attributes were categorised into three importance levels. Three statistically significant attributes were identified ("I earn my patient's trust by making suitable communication," "I receive support from my instructors," and "I try to endure academic hardship") based on comparison with shadow attributes. The rough set analysis showed nine main behavioural patterns. Random forest, support vector machines, and backpropagation artificial neural networks were used to test the performance of the model, with accuracies ranging from 73.0% to 76.9%. Our results provide possible strategies for improving academic resilience and competence of nursing students.

Covering rough set models, fuzzy rough set models and soft rough set models induced by covering similarity

In order to study the rule of quantity characteristics of rough set theory, a novel notion of covering similarity in a covering approximation space is introduced. Based on this new notion, novel covering rough set models, novel fuzzy rough set models and novel soft rough set models are defined. Further, the concept of similarity matrix is presented, and then the matrix calculation formulas of all the new concepts and new models are deduced. Moreover, aim to better solve the practical problems by comparing the similarity, a concept of similarity interval is introduced, then the covering classification is carried out and a partial order of all the classes is derived. Finally, a few practical examples are given to illustrate the simple applications of all the new concepts and models in this paper.

Measurement and distribution characteristics of urban green productivity: based on fuzzy rough set and spatial autocorrelation analysis

Measurement and distribution characteristics of urban green productivity: based on fuzzy rough set and spatial autocorrelation analysis

Noise-resistant fuzzy multineighbourhood rough set-based feature selection with label enhancement and its application for multilabel classification

Noise pollution in process of feature selection greatly reduces the classification efficacy of multilabel data, and the estimation of label descriptions is insufficient because of the lack of label enhancement for label distribution learning. To overcome the limitations, this work presents a noise-resistant fuzzy multineighbourhood rough set-based feature selection methodology with label enhancement and its application for multilabel classification. First, under the fuzzy T-equivalence relation, to construct new adaptive fuzzy neighbourhood class, the multineighbourhood radius set on feature space is integrated with fuzzy neighbourhood radius via sample interval on label space; then, the adaptive fuzzy multineighbourhood granules are obtained. Second, by integrating the parameterized fuzzy decision of labels and adaptive fuzzy multineighbourhood granules, noise-resistant lower and upper approximations via fuzzy multineighbourhood are constructed. A noise-resistant multilabel fuzzy multineighbourhood rough set model and its noise-resistant approximate accuracy can be proposed. Third, the description degree of label is computed to design label proportion after label enhancement. Certain fuzzy multineighbourhood entropy measures are developed to obtain label enhancement-based mutual information. When the algebraic and information perspectives are fused, label enhancement-based mutual information with fuzzy multineighbourhood via approximate accuracy is presented to evaluate the final association relationship between the features and label sets. Finally, a multilabel feature selection strategy via label enhancement will be constructed to obtain the best feature subset. The experimental results applied to 14 multilabel datasets indicate that this algorithm is significant.

Analysis of influencing factors of traffic accidents on urban ring road based on the SVM model optimized by Bayesian method.

Based on small scale sample of accident data from specific scenarios, fully exploring the potential influencing factors of the severity of traffic accidents has become a key and effective research method. In order to analyze the factors mentioned above in the scenario of urban ring roads, this paper collected data records of 1250 traffic accidents involving different severity on urban ring road of a central city in northwest China in the past 3 years. Firstly, the Support Vector Machine (SVM) model of non-parametric method is utilized to analyze the data above, and three kernel functions of linear, inhomogeneous polynomial and Gaussian radial basis are constructed respectively. Considering comprehensively 16 potential influencing factors covering the driver-vehicle-road-environment integrated system, the SVM models of above three kernel functions are verified, accuracy reaches 0.771 and F1 reaches 0.841. Then, Bayesian Optimization (BO), Grids Search (GS) and Rough Set (RS) are utilized as optimizer to adjust the parameters of Gaussian radial basis function SVM model, the performance of BO-SVM is further improved and reaches the optimum, with an average accuracy of 0.875 on the test set and a F1 of 0.886, completely outperforming the benchmark models of GS-SVM, RS-SVM, Bilayer-LSTM and BP. Finally, the sensitivity analysis method is utilized to quantify the sensitivity of the potential influencing factors to the severity of road accidents, and the backward selection method is utilized to screen the core influencing factors that influence the severity of accident, concluded that core influencing factors are age, driving mileage and vehicle type. This paper will provide reference for the analysis of the significant influencing factors for road accidents severity, and to provide theoretical support for the precise formulation of accident improvement strategies.

A Method to Handle the Missing Values in Multi-Criteria Sorting Problems Based on Dominance Rough Sets

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of `do not care’ type missing values.

Intuitionistic Fuzzy Sequential Three-Way Decision Model in Incomplete Information Systems

As an effective method for uncertain knowledge discovery and decision-making, the three-way decisions model has attracted extensive attention from scholars. However, in practice, the existing sequential three-way decision model often faces challenges due to factors such as missing data and unbalanced attribute granularity. To address these issues, we propose an intuitionistic fuzzy sequential three-way decision (IFSTWD) model, which introduces several significant contributions: (1) New intuitionistic fuzzy similarity relations. By integrating possibility theory, our model defines similarity and dissimilarity in incomplete information systems, establishing new intuitionistic fuzzy similarity relations and their cut relations. (2) Granulation method innovation. We propose a density neighborhood-based granulation method to partition decision attributes and introduce a novel criterion for evaluating attribute importance. (3) Enhanced decision process. By incorporating sequential three-way decision theory and developing a multi-level granularity structure, our model replaces the traditional equivalent relation in the decision-theoretic rough sets model, thus advancing the model’s applicability and effectiveness. The practical utility of our model is demonstrated through an example analysis of “Chinese + vocational skills” talent competency and validated through simulation experiments on the UCI dataset, showing superior performance compared to existing methods.

New Insights into Rough Set Theory: Transitive Neighborhoods and Approximations

Rough set theory is a methodology that defines the definite or probable membership of an element for exploring data with uncertainty and incompleteness. It classifies data sets using lower and upper approximations to model uncertainty and missing information. To contribute to this goal, this study presents a newer approach to the concept of rough sets by introducing a new type of neighborhood called j-transitive neighborhood or j-TN. Some of the basic properties of j-transitive neighborhoods are studied. Also, approximations are obtained through j-TN, and the relationships between them are investigated. It is proven that these approaches provide almost all the properties provided by the approaches given by Pawlak. This study also defines the concepts of lower and upper approximations from the topological view and compares them with some existing topological structures in the literature. In addition, the applicability of the j-TN framework is demonstrated in a medical scenario. The approach proposed here represents a new view in the design of rough set theory and its practical applications to develop the appropriate strategy to handle uncertainty while performing data analysis.