Showing papers on "Rough set published in 2021"

Feature Selection Using Fuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Sets

Abstract: For heterogeneous data sets containing numerical and symbolic feature values, feature selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very significant step to preprocess data and improve its classification performance. This article presents an FNMRS-based feature selection approach in neighborhood decision systems. First, some concepts of fuzzy neighborhood rough sets and neighborhood multigranulation rough sets are given, and then the FNMRS model is investigated to construct uncertainty measures. Second, the optimistic and pessimistic FNMRS models are built by using fuzzy neighborhood multigranulation lower and upper approximations from algebra view, and some fuzzy neighborhood entropy-based uncertainty measures are developed in information view. Inspired by both algebra and information views based on the FNMRS model, the fuzzy neighborhood pessimistic multigranulation entropy is proposed. Third, the Fisher score model is utilized to delete irrelevant features to decrease the complexity of high-dimensional data sets, and then, a forward feature selection algorithm is provided to promote the performance of heterogeneous data classification. Experimental results on 12 data sets show that the presented model is effective for selecting important features with the higher stability of classification in neighborhood decision systems.

On Multicriteria Decision-Making Method Based on a Fuzzy Rough Set Model With Fuzzy $\alpha$ -Neighborhoods

Abstract: In this article, a novel fuzzy $\alpha$ -neighborhood operator with reflexivity is constructed and a new fuzzy rough set model based on the fuzzy $\alpha$ -neighborhood operator is proposed. Aiming at decision-making in information systems with real-valued information systems (RVISs), we first utilize data normalization method to effectively transform RVISs into information systems with fuzzy-valued information systems (FVISs). Then, we use the fuzzy $\alpha$ -neighborhood-based fuzzy rough set model to convert FVISs into information systems with intuitionistic fuzzy-valued information systems (IFVISs). By adopting the idea of the PROMETHEE II method, we develop three different sorting decision-making schemes on IFVISs, which consist of the subtraction of intuitionistic fuzzy numbers, sorting functions, and intimacy coefficients. Finally, numerical experiments demonstrate the effectiveness of our method. Comparative studies and Spearman rank correlation analyses explain the superiority of our schemes. Experimental results verify the stability of the performance of our strategy.

Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory

Abstract: Since the future of the society depends upon the role of students and their services construct the prosperous and advanced society, so suitable career selection for the students' is considered to be an important problem to explore. As per psychology, if a student has the required capability, and positive attitudes towards a subject in terms of interest, attitude, memory, knowledge, environment, and career, then the student will achieve more in that subject. To consider this kind of uncertain issues, picture fuzzy set and rough set are found to be appropriate due to their inherent characteristics to deal with incomplete and imprecise information. In this study, picture fuzzy set and rough set-based approaches are proposed to help the student to choose an appropriate subject and consequently provide a good service or contribution to the society particularly in that domain. The main purpose of the article is to analyse student's features in terms of career, memory, interest, knowledge, environment and attitude and then predict the appropriate stream for making the career comfortable so that the student can conveniently explore much in that area. To select students' career, a hybridized distance measure under picture fuzzy environment is proposed where the evaluating information regarding students, subjects and student's features are given in picture fuzzy numbers. In this paper, two types of hybridization approaches are proposed which are the hybridization of Hausdorff and Hamming distance measures and hybridization of Hausdorff and Euclidean distance measures. Next, we apply rough set theory to determine whether a particular subject is appropriate for a student even if there is controversy to select a stream. Lower and higher approximation with boundary region of rough set theory is used to manage inconsistency situations. Finally, two case studies are demonstrated to validate the applicability of the proposed idea.

A Sequential Three-Way Decision Model With Intuitionistic Fuzzy Numbers

Abstract: Three-way decision model (3WDM) with decision-theoretical rough sets (DTRSs) always addresses precise cost parameters and precise attribute values in uncertain problem solving. However, intuitionistic fuzzy sets (IFSs), as extensions of fuzzy sets, are described by dual parameters, namely the membership and nonmembership degrees. Therefore, under intuitionistic fuzzy environments, the 3WDM confronted great challenges when intuitionistic fuzzy number (IFN) cost parameters and IFN attribute values arise together. In this paper, by considering the impacts of both the IFN cost parameters and the IFN attribute values, new 3WDMs with IFNs are constructed from the membership degree and nonmembership degree perspectives, respectively. Then optimistic and pessimistic 3WDMs with IFNs are, respectively, established based on different risk preferences for more accurate decisions. Subsequently, by introducing a sequential strategy, the presented models are applied to address attribute increments in practical problems. Finally, we present a numerical example and some experiments to validate the efficiency of sequential 3WDMs.

Set-theoretic models of three-way decision

Abstract: The theory of three-way decision is about a philosophy of thinking in threes, a methodology of working with threes, and a mechanism of processing in threes. We approach a whole through three parts, in terms of three units, or from three perspectives. A trisecting–acting–outcome (TAO) model of three-way decision involves trisecting a whole into three parts and acting on the three parts, in order to produce an optimal outcome. In this paper, we further explore the TAO model in a set-theoretic setting and make three new contributions. The first contribution is an examination of three-way decision with nonstandard sets for representing concepts under the two kinds of objective/ontic and subjective/epistemic uncertainty. The second contribution is an introduction of an evaluation-based framework of three-way decision. We present a classification of trisections and investigate the notion of an evaluation space. The third contribution is, within the proposed framework, a systematical study of three-way decision with rough sets, interval sets, fuzzy sets, shadowed sets, rough fuzzy sets, interval fuzzy sets (or equivalently, vague sets, interval-valued fuzzy sets, intuitionistic fuzzy sets), and soft sets.

A novel approach to attribute reduction based on weighted neighborhood rough sets

Abstract: Neighborhood rough sets based attribute reduction, as a common dimension reduction method, has been widely used in machine learning and data mining. Each attribute has the same weight (the degree of importance) in the existing neighborhood rough set models. In this work, we introduce different weights into neighborhood relations and propose a novel approach for attribute reduction. The main motivation is to fully mine the correlation between attributes and decisions before calculating neighborhood relations, and the attributes with high correlation are assigned higher weights. We first construct a Weighted Neighborhood Rough Set (WNRS) model based on weighted neighborhood relations and discuss its properties. Then WNRS based dependency is defined to evaluate the significance of attribute subsets. We design a greedy search algorithm based on WNRS to select an attribute subset which has both strong correlation and high dependency. Furthermore, we use isometric search to find the optimal neighborhood threshold. Finally, ten datasets from UCI machine learning repository and ELVIRA Biomedical data set repository are used to compare the performance of WNRS with those of other state-of-the-art reduction algorithms. The experimental results show that WNRS is feasible and effective, which has higher classification accuracy and compression ratio.

Feature Selection with Missing Labels Using Multilabel Fuzzy Neighborhood Rough Sets and Maximum Relevance Minimum Redundancy

Abstract: Recently, multilabel classification has generated considerable research interest. However, the high dimensionality of multilabel data incurs high costs; moreover, in many real applications, a number of labels of training samples are randomly missed. Thus, multilabel classification can have great complexity and ambiguity, which means some feature selection methods exhibit poor robustness and yield low prediction accuracy. To solve these issues, this paper presents a novel feature selection method based on multilabel fuzzy neighborhood rough sets (MFNRS) and maximum relevance minimum redundancy (MRMR) that can be used on multilabel data with missing labels. First, to handle multilabel data with missing labels, a relation coefficient of samples, label complement matrix, and label-specific feature matrix are constructed and implemented in a linear regression model to recover missing labels. Second, the margin-based fuzzy neighborhood radius, fuzzy neighborhood similarity relationship, and fuzzy neighborhood information granule are developed. The MFNRS model is built based on multilabel neighborhood rough sets combined with fuzzy neighborhood rough sets. Based on algebra and information views, certain fuzzy neighborhood entropy-based uncertainty measures are proposed for MFNRS. The fuzzy neighborhood mutual information-based MRMR model with label correlation is improved to evaluate the performance of candidate features. Finally, a feature selection algorithm is designed to improve the performance for multilabel data with missing labels. Experiments on twenty datasets verify that our method is effective not only for recovering missing labels but also for selecting significant features with better classification performance.

Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems

Abstract: The main task of local rough set model is to avoid the interference of complicated calculation and invalid information in the formation of approximation space. In this paper, we first present a local rough set model based on dominance relation to make the local rough set theory applicable to the ordered information system, then two kinds of local multigranulation rough set models in the ordered information system are constructed by extending the single granulation environment to a multigranulation case. Moreover, the updating processes of dynamic objects based on global (classical) and local multigranulation rough sets in the ordered information system are analyzed and compared carefully. It is addressed about how the rough approximation spaces of global multigranulation rough set and local multigranulation rough set change when the object set increase or decrease in an ordered information system. The relevant algorithms for updating approximations with dynamic objects on global and local multigranulation rough sets are provided in ordered information systems. To illustrate the superiority and the effectiveness of the proposed dynamic updating approaches in the ordered information system, experimental evaluation is performed using six datasets coming from the University of California-Irvine repository.

Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

Abstract: Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks The segmentation process involves finding similar regions within an image Many segmentation problems are achieved by the incorporation of clustering techniques One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA) In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image Under such circumstances, the occurrence of misclassification of pixels is reduced A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs

EDA S Method for Multi-Criteria Group Decision Making Based on Intuitionistic Fuzzy Rough Aggregation Operators

Abstract: The primitive notions of rough sets and intuitionistic fuzzy set (IFS) are general mathematical tools having the ability to handle the uncertain and imprecise knowledge easily. EDA $\mathcal {S}$ (Evaluation based on distance from average solution) method has a significant role in decision making problems, especially when more conflicting criteria exist in multicriteria group decision making (MCGDM) problems. The aim of this manuscript is to present intuitionistic fuzzy rough- EDA $\mathcal {S}$ (IFR- EDA $\mathcal {S}$ ) method based on IF rough averaging and geometric aggregation operators. In addition, we put forward the concept of IF rough weighted averaging (IFRWA), IF rough ordered weighted averaging (IFROWA) and IF rough hybrid averaging (IFRHA) aggregation operators. Furthermore, the concepts of IF rough weighted geometric (IFRWG), IF rough ordered weighted geometric (IFROWG) and IF rough hybrid geometric (IFRHG) aggregation operators are investigated. The basic desirable characteristics of the investigated operator are given in detail. A new score and accuracy functions are defined for the proposed operators. Next, IFR-EDA $\mathcal {S}$ model for MCGDM and their stepwise algorithm are demonstrated by utilizing the proposed approach. Finally, a numerical example for the developed model is presented and a comparative study of the investigated models with some existing methods are expressed broadly which show that the investigated models are more effective and useful than the existing approaches.

Incremental Feature Selection Using a Conditional Entropy Based on Fuzzy Dominance Neighborhood Rough Sets

Abstract: Incremental feature selection approaches can improve the efficiency of feature selection used for dynamic datasets, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic ordered data. Moreover, the monotonic classification effect of ordered data is easily affected by noise, so a robust feature evaluation metric is needed for feature selection algorithm. Motivated by these two issues, we investigate incremental feature selection approaches using a new conditional entropy with robustness for dynamic ordered data in this study. First, we propose a new rough set model, $i.e.$ , fuzzy dominance neighborhood rough sets (FDNRS). Second, a conditional entropy with robustness is defined based on FDNRS model, which is used as evaluation metric for features and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are designed on the basis of the above researches. Experiments are performed on ten public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic ordered data.

A novel hybrid feature selection method considering feature interaction in neighborhood rough set

Abstract: The interaction between features can provide essential information that affects the performances of learning models. Nevertheless, most feature selection methods do not take interaction into account in feature correlations calculation. In this work, to solve the problem of dimensional reduction in hybrid data with uncertainty and noise, a novel feature selection method is proposed considering the characteristic of interaction in the neighborhood rough set. First of all, the multi-neighborhood radii set for hybrid data is obtained according to the distribution characteristics of features. Then, considering the ubiquity of interactive features, the feature correlations are redefined via employing various neighborhood information uncertainty measures. Furthermore, a new objective evaluation function of the interactive selection of hybrid features is developed, which is called the Max-Relevance min-Redundancy Max-Interaction (MRmRMI). Finally, a novel interaction feature selection algorithm based on neighborhood conditional mutual information (NCMI_IFS) is designed. To evaluate the performance of the proposed algorithm, we compare it with other eight representative feature selection algorithms on twenty public datasets. Experimental results on four different classifiers show that the NCMI_IFS algorithm has higher classification performance and is significantly effective.

Rough Sets in COVID-19 to Predict Symptomatic Cases

Abstract: Rough set theory is a new mathematical or set-theoretical practice to study inadequate knowledge. There are many use cases in the real world where there is a lack of crisp knowledge. In view of this, many Scientists have been attempted to address anomalies associated with imperfect knowledge for a long time. In recent times, computer and mathematics researchers have been trying to resolve this decisive issue, mainly in artificial intelligence province. The COVID-19 pandemic encroaches the harmony of the whole world. Many patients of COVID-19 have different symptoms, so it is very difficult to carry out the symptoms-based prediction COVID-19. However, the rough set theory approach help to minimize the number of attributes from the underlined decision table. This work defines the decision table having patients and symptoms of the COVID-19 in the rows and columns respectively. By studying data indiscernibility, elementary sets are specified for each attribute. Moreover, lower approximation, upper approximation, class of rough sets and accuracy of approximation are defined for different individual or group symptoms. This proposed work investigates whether particular symptoms belong to the decision set or not and also the accuracy of observations is calculated and analyzed. The probability of having COVID-19 is defined by considering the different sets of attributes. The main objective of this work is to minimize the number of symptoms of COVID-19 by rough set theory approach for better decision making. This symptoms-based prediction could help us while checking patients and decision-makers could be benefited while making policies and guidelines.

Information Granulation-Based Community Detection for Social Networks

Abstract: Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world’s population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect communities in the OSNs using the concepts of granular computing in rough sets is proposed. In this information model, a social network as a rough set granular social network (RGSN) is modeled. A new community detection algorithm named granular-based community detection (GBCD) is implemented. This article also defines and uses two measures, namely, a granular community factor and an object community factor. The proposed algorithm is evaluated on four real-world data sets as well as computer-generated data sets. The model is compared with other state-of-the-art community detection algorithms for the values of modularity, normalized mutual information (NMI), Omega index, accuracy, specificity, sensitivity, and $F1$ -measure. The cumulative performance of the GBCD algorithm is found to be 3.99, which outperforms other state-of-the-art community detection algorithms.

Performance evaluation of manufacturing collaborative logistics based on BP neural network and rough set

Abstract: The collaborative logistics in manufacturing industry has a greater impact on its operation effect, and there are many hidden factors. In order to improve the performance evaluation of manufacturing collaborative logistics, this study builds a combined performance evaluation model based on BP neural network and rough set. Moreover, this study uses the rough set attribute reduction theory to screen and optimize the evaluation indicators to obtain the key performance indicator set, and then uses BP neural network to predict and evaluate the key performance indicator data, which greatly reduces the number of training times and shortens the learning time. In addition, in this study, a case analysis was used to solve the performance evaluation model of manufacturing collaborative logistics based on rough set and BP neural network, and corresponding strategies were given. The research results show that the method proposed in this paper has certain effects.

A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Abstract: Breast cancer is one of the significant tumor death in women Computer-aided diagnosis (CAD) supports the radiologists in recognizing the irregularities in an efficient manner In this work, a novel CAD system proposed for mammogram image analysis based on grey wolf optimizer (GWO) with rough set theory Texture, intensity, and shape-based features are extracted from mass segmented mammogram images To derive the appropriate features from the extracted feature set, a novel dimensionality reduction algorithm is proposed based on GWO with rough set theory GWO is a novel bio-inspired optimization algorithm, stimulated based on hunting activities and social hierarchy of the grey wolves In this paper, a hybridization of GWO and Rough Set (GWORS) methods are used to find the significant features from the extracted mammogram images To evaluate the effectiveness of the proposed GWORS, we compare it with other well-known rough set and bio-inspired feature selection algorithms including particle swarm optimize, genetic algorithm, Quick Reduct and Relative Reduct From empirical results, it is observed that the proposed GWORS outperforms the other techniques in terms of accuracy, F-Measures and receiver operating characteristic curve

Neighborhood rough sets with distance metric learning for feature selection

Abstract: Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data. Feature selection based on neighborhood rough set has been studied widely. However, most existing methods use a single predefined distance function to construct neighborhood granules. As not all datasets are created with the same way and data are also often disturbed with noisy, the same distance function may not be optimal for all datasets. This paper aims at improving the discriminative ability and decreasing the uncertainty in the representation from neighborhood rough set to deal with this issue. In this paper, distance learning method is first introduced into neighborhood rough set to optimize the structure of information granules. A novel neighborhood rough set model is then proposed, called Neighborhood rough set Model based on Distance metric learning (NMD). NMD exploits distance metric learning in which samples from the same decision achieve small distance than samples from different decisions. Such a method can improve the consistency of neighborhood granules. The paper also presents the properties of NMD and formulates the importance of feature. In addition, two feature selection algorithms are built upon the proposed NMD. Experimental results on real-world datasets demonstrate the effectiveness of the proposed feature selection algorithms and their superiority against comparison baselines.

An integrated rough ELECTRE II approach for risk evaluation and effects analysis in automatic manufacturing process

Abstract: Smart manufacturing is an essential part of fourth industrial revolution in which robotic machines can control and perceive automatically to provide effectiveness and convenience in production process. However, the existence of potential failures and defects not only influence the manufacturing process but also damages the resources and cause negative impacts on environment. Failure modes and effects analysis (FMEA) is a key approach to identify and eliminate possible failures and evaluate the risks from design, system and process. This research paper provides a novel FMEA approach for risk evaluation by integrating rough set theory and ELimination and Choice Translating REality (ELECTRE) II method to handle the subjectivity and uncertainty in experts’ judgements without much prior information, membership functions and additional adjustments. Rough numbers are used to study uncertainty in linguistic terms using intervals instead of single fixed values. The proposed approach is formulated by defining different types of concordance and discordance sets using optimization techniques based on statistical dispersion and maximum deviation method. The presented technique shows the strong, weak and neutral pairwise relations among failure modes by systemically comparing them from each risk component. The distance functions and averaging methods are applied to check the similarities and differences among error modes which improves the accuracy of the results. The developed rough FMEA approach is applied to identify the potential failures of robot working in optical cable industry and evaluate the risk components of manufacturing and production process. Rough ELECTRE II approach can be effectively applied to enhance the efficiency of working conditions and prevent the loss of crude materials and energy.

Rough Best-Worst Method for Supplier Selection in Biofuel Companies based on Green criteria

Abstract: This paper concerns with the integration of rough set theory with the Best Worst method to evaluate information system performance within supplier selection problem of biofuel companies. First, a set of main criteria and sub-criteria are collected and then to include uncertainty in decision making, rough set theory is employed. The rough best worst method is applied for weighing and supplier evaluation with respect to information system performance and environmental impacts. Further, a case study is conducted for biofuel company supplier selection and the results imply the effectiveness of the approach in tactical performance evaluation. The best criteria effective on the green supplier selection of ISs performance is determined to be Quality.

Improvement of the approximations and accuracy measure of a rough set using somewhere dense sets

Abstract: Rough set theory is a non-statistical approach to handle uncertainty and uncertain knowledge. It is characterized by two methods called classification (lower and upper approximations) and accuracy measure. The closeness of notions and results in topology and rough set theory motivates researchers to explore the topological aspects and their applications in rough set theory. To contribute to this area, this paper applies a topological concept called “somewhere dense sets” to improve the approximations and accuracy measure in rough set theory. We firstly discuss further topological properties of somewhere dense and cs-dense sets and give explicitly formulations to calculate S-interior and S-closure operators. Then, we utilize these two sets to define new concepts in rough set context such as SD-lower and SD-upper approximations, SD-boundary region, and SD-accuracy measure of a subset. We establish the fundamental properties of these concepts as well as show their relationships with the previous ones. In the end, we compare the current method of approximations with the previous ones and provide two examples to elucidate that the current method is more accurate.

Soft β-rough sets and their application to determine COVID-19

Abstract: Soft rough set theory has been presented as a basic mathematical model for decision-making for many real-life data However, soft rough sets are based on a possible fusion of rough sets and soft sets which were proposed by Feng et al [20] The main contribution of the present article is to introduce a modification and a generalization for Feng's approximations, namely, soft β -rough approximations, and some of their properties will be studied A comparison between the suggested approximations and the previous one [20] will be discussed Some examples are prepared to display the validness of these proposals Finally, we put an actual example of the infections of coronavirus (COVID-19) based on soft β -rough sets This application aims to know the persons most likely to be infected with COVID-19 via soft β -rough approximations and soft β -rough topologies [ABSTRACT FROM AUTHOR] Copyright of Turkish Journal of Mathematics is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )