Application Of Rough Set Theory Research Articles

Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory

The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature.

An application of rough set theory to predict telecom customer churn

An application of rough set theory to predict telecom customer churn

Extension of topological structures using lattices and rough sets

<abstract><p>This paper explores the application of rough set theory in analyzing ambiguous data within complete information systems. The study extends topological structures using equivalence relations, establishing an extension of topological lattice within lattices. Various relations on topological spaces generate different forms of exact and rough lattices. Building on Zhou's work, the research investigates rough sets within the extension topological lattice and explores the isomorphism between topology and its extension. Additionally, the paper investigates the integration of lattices and rough sets, essential mathematical tools widely used in problem-solving. Focusing on computer science's prominent lattices and Pawlak's rough sets, the study introduces extension lattices, emphasizing lower and upper extension approximations' adaptability for practical applications. These approximations enhance pattern recognition and model uncertain data with finer granularity. While acknowledging the benefits, the paper stresses the importance of empirical validations for domain-specific efficacy. It also highlights the isomorphism between topology and its extension, revealing implications for data representation, decision-making, and computational efficiency. This isomorphism facilitates accurate data representations and streamlines computations, contributing to improved efficiency. The study enhances the understanding of integrating lattices and rough sets, offering potential applications in data analysis, decision support systems, and computational modeling.</p></abstract>

An application of rough set theory to predict telecom customer churn

An application of rough set theory to predict telecom customer churn

Curriculum System Design and Teaching Mode Innovation for Specialized College English Platforms

Abstract This study addresses the application of rough set theory in English hierarchical teaching and explores how to optimize the teaching mode and improve the teaching quality and learning effect through this theory. This paper adopts a literature analysis and questionnaire survey method to study rough set theory’s knowledge expression and systematic decision-making mechanism. It applies it to the design of the curriculum system and the innovation of the teaching mode in the English platform of special-purpose universities. The optimized English hierarchical teaching mode using rough set theory significantly improved students’ learning interest and learning effect, in which 87.53% of the students believed it could improve their mastery of knowledge. The average score of 90.3 was 23.4 points higher than that of the control group, which was statistically significant (P<0.05). This study also found that the level of teacher specialization, students’ study habits, and school policies are key factors that influence the implementation of the teaching model. The introduction of rough set theory in this study provides new perspectives and methods for English teaching, promoting innovation in teaching methods and improving students’ learning ability.

Modelling of similarity characteristics of polycyclic aromatic hydrocarbons (PAHs) in Sungai Perak, Malaysia via rough set theory and principal component analysis (PCA)

This paper presents application of rough set theory and PCA for modelling of similarity characteristics of PAHs from Perak River: Tanjung Belanja Bridge (TBB, l1), Water Treatment Plant Parit (WTPP, l2), Parit Town Discharge (PTD, l3), Water Treatment Plant Senin (WTPS, l4), and Water Treatment Plant Kepayang (WTPK, l5). The clustering involved PAHs as attributes and sampling stations as objects. The l1 and l3 were clustered in Simdegx,y≥0.8. The Simdegx,y≥0.7 was observed in all attributes (except l5) forming two sets of clusters. PCA showed that low molecular weight (LMW) PAHs were prominent with variances of 68.47% (naphthalene, Nap) and 27.63% (carbazole).

Application of Rough Set Theory and Bow-Tie Analysis to Maritime Safety Analysis Management: A Case Study of Taiwan Ship Collision Incidents

The surrounding waters of Taiwan are evaluated as a moderate risk environment by Casualty Return, Lloyd’s Registry of Shipping. Among all types of maritime accidents, ship collisions occur most often, which has severe consequences, including ship damage, sinking and death of crews, and destruction of marine environments. It is, therefore, imperative to mitigate the risk of ship collision by exploring the risk factors and then providing preventive measures. This study invited domain experts to form a decision-making group, which helped with the risk assessment. The initial set of risk factors was selected from the literature. The expert group then identified seven representative risk factors using rough set theory (RST). The researchers worked with the experts to delineate the diagram of a bow-tie analysis (BTA), which provided the causes, consequences, and preventive and mitigation measures for ship collision incidents. The results show an integrated research framework for the risk assessment of ship collision that can effectively identify key factors and associated managerial strategies to improve navigation safety, leading to a sound marine environment.

Modeling adoption of genetically modified foods: Application of Rough Set Theory and Flow Network Graph

IntroductionThe main purpose of this study is to extract the rules and patterns governing the behavioral intention of consumers towards the adoption of genetically modified foods (GMFs).MethodThe proposed method is a combination of Rough Set Theory (RST) and Flow Network Graph (FNG). Data was collected from 386 consumers to extract rough rules. 13 rules have been chosen from 289 original rules that were divided into three groups: low, medium, and high intention to use GMFs. They were chosen because of the support values and other indexes that were used in the RST. Eventually, to interpret the performance of the generated rules, FNG were illustrated for each decision-making class, and seven patterns were extracted.ResultsThe findings confirm that corporate social responsibilities, consumer concerns, occupational status, and consumer autonomy are more important than other observed dimensions in consumers' decision-making. Moreover, the findings illustrate that combining Rough Set Theory and Flow Network Graph could predict customers' intentions and provide valuable information for policy-makers in related active industries.DiscussionBased on the analysis outcomes, the most significant factors that affect consumers' intention to use GMFs are: “consumer perception of CSR”; “consumer concerns”; “occupational status”; and “consumer autonomy”. Thus, managers and policymakers must pay more attention to these concepts when they survey consumer intention behavior.

Application of rough set theory to identify priorities in project management

The purpose of this paper is to demonstrate the use of Rough Set Theory to identify information that can be used in project management, namely the knowledge about the appropriate prioritization of activities and the use of resources directed towards the adopted project objectives. Rough Set Theory operates on large data sets and permits the reduction of irrelevant data and the induction of decision rules that discover recursion and relationships in the data. The discovered rules can prove valuable for decision-making project managers. All the rules found in course of the proposed rule-finding process have been considered interesting and are discussed in the paper. The paper also provides an example of the application of Rough Set Theory in the field mentioned above.

Exploiting fuzzy rough mutual information for feature selection

Feature selection is one of the important applications of rough set theory. Rough entropy proposed in rough set theory has been applied to feature selection. However, rough entropy is based on binary equivalence relation to divide object sets. Therefore, it applies only to nominal attribute data. To this end, this paper extends rough entropy to fuzzy rough set theory, and then proposes the fuzzy rough entropy in fuzzy approximate space. Fuzzy rough entropy decreases monotonically with the increase of the number of features. On this basis, the concepts of fuzzy joint rough entropy, fuzzy conditional rough entropy and fuzzy rough mutual information are defined. In order to measure the importance of features, inner and outer significance functions are constructed by making use of fuzzy rough mutual information. Furthermore, based on the proposed fuzzy rough entropy model, the corresponding feature selection algorithm is designed. It can directly deal with not only nominal data, but also numerical data and even mixed data. The use of inner and outer heuristic significance functions makes the proposed method select features from two complementary perspectives, so that the reduced feature set has better classification performance. In this method, the redundant features are effectively deleted by the backward redundancy elimination strategy. The proposed algorithm is compared with other algorithms on public data. The experimental results show that the proposed method is adaptive and effective.

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

SummaryRecent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

Categorification of a set relation geometry induced by rough operators

In this paper, we investigate Pawlak’s rough set theory from a categorical point of view, by introducing specific categories of lower and upper operators in order to analyze in a generalized setting the usual approximant operators of rough set theory. We determine several embeddings and isomorphisms between these categories and suitable categories of finitary matroids, set partitions and equivalence relations, some of which already investigated in recent papers. Using the aforementioned isomorphic categories, we exhibit several categorical properties of lower and upper operators. In addition, as one of the main applications of rough set theory concerns Pawlak’s information systems and Granular Computing, in the last part of the paper we translate in categorical terms the occurrence of rough sets in Granular Computing and, to this end, we need to work with a category PR of pairings (i.e. generalizations of Pawlak’s information systems) and pairing homomorphisms. More specifically, we exhibit several categorical properties of pairings, such as balancedness, completeness, exactness, (RegEpi,Mono-Source)-factorizability and prove that PR is Heyting but, in general, it does not admit coproducts.

A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets

Attribute reduction is an important application of rough set theory. Most existing rough set models do not consider the weight information of attributes in information systems. In this paper, we first study the weight of each attribute in information systems by using data binning method and information entropy theory. Then, we propose a new rough set model of weighted neighborhood probabilistic rough sets (WNPRSs) and investigate its basic properties. Meanwhile, the dependency degree formula of an attribute relative to an attribute subset is defined based on WNPRSs. Subsequently, we design a novel attribute reduction method by using WNPRSs and the corresponding algorithm is also given. Finally, to evaluate the performance of the proposed algorithm, we conduct a data experiment and compare it with other existing attribute reduction algorithms on eight public datasets. Experimental result demonstrates that the proposed attribute reduction algorithm is effective and performs better than some of the existing algorithms.

Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.

Purpose: To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients.Methods: The official satisfaction questionnaire developed by the National Health Commission of the People's Republic of China was used. Rough set theory was used to identify the perception patterns between condition attributes (i.e., service factors) and a decision attribute (i.e., overall service level) and to express them in rule form (i.e., if-then).Results: The four minimal-coverage rules, with strength exceeding 10% in the good class, and six crucial condition attributes were obtained: “Ease of registration (C1),” “Respected by registered staff (C2),” “Registered staff's listening (C3),” “Respected by doctor (C9),” “Signpost (C12),” and “Privacy (C16).” In addition, the average hit rate for 5-fold cross-validation was 90.86%.Conclusions: A series of decision rules could help decision-makers easily understand outpatients' situations and propose more suitable programs for improving hospital service quality because these decision rules are based on actual outpatient experiences.

Uncertainty representation in dynamical systems using rough set theory

In this article, we study the application of Rough Set theory to the representation of uncertainty and partial knowledge in Dynamical Systems. Our approach draws from the abstract notion of an observable pattern, and for this purpose we first propose an abstract knowledge representation formalism that encompasses the main classes of discrete Dynamical Systems. Drawing on the proposed representational formalism, we define appropriate notions of rough approximations and reducts, show how these can be applied for uncertainty representation, and discuss their theoretical properties and characterizations.

Estimation of raw silk quality using rough set theory

Being an agro based natural product, silk always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of raw silk quality from the imprecise cocoon parameters is an intricate problem. Rough set theory has evolved as one of the most important technique used for handling imprecise data. One of the cardinal uses of rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for the applications of rough set theory. Our approach focuses on the elimination of the redundant data set in order to generate the effective decision rule which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict renditta from five cocoon parameters such as shell ratio, defective cocoon percentage, cocoon volume, cocoon weight and filament length.

Estimating Stock Returns Using Rough Set Theory: An Exploratory study With An Evidence From Iraq Stock Exchange

‎ This research aims to estimate stock returns, according to the ‎Rough Set Theory ‎approach, ‎test ‎its effectiveness and accuracy in predicting stock returns and their potential in the ‎field of ‎financial ‎markets, and rationalize investor decisions. The research sample is totaling (10) ‎companies traded at Iraq Stock Exchange. The results showed a remarkable ‎ ‎Rough Set Theory application in data reduction, contributing to the rationalization of ‎investment ‎decisions. The most prominent conclusions are the capability of rough set theory ‎in ‎dealing with financial data and applying it for forecasting stock ‎returns.‎The ‎research provides those interested in investing stocks in financial markets ‎with ‎significant financial analysis tools that exceed the traditional statistical methods. ‎The ‎originality ‎of the research lies in the diversification of financial and statistical analysis tools ‎and ‎methods of ‎forecasting stock returns

Attribute selection for heterogeneous data based on information entropy

Attribute selection in an information system is one of the important applications of rough set theory. This paper studies attribute selection for heterogeneous data based on information entropy. We first define information entropy in an information system with heterogeneous data and then put forward the notions of joint information entropy, conditional information entropy and mutual information entropy in a decision information system with heterogeneous data. We apply information entropy to perform attribute selection in a decision information system with heterogeneous data. We propose two attribute selection algorithms based on information entropy. Finally, we make experimental analysis and comparisons to illustrate the feasibility and efficiency of the proposed algorithms.

A rough set theory application in forecasting models

This paper introduces the performance of different forecasting methods for tourism demand, which can be employed as one of the statistical tools for time series forecasting. The Holt-Winters (HW), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Grey model (GM (1, 1)) are three important statistical models in time-series forecasting. This paper analyzes and compare the performance of forecasting models using rough set methods, Total Roughness (TR), Min-Min Roughness (MMR) and Maximum Dependency of attributes (MDA). Current research identifies the best time series forecasting model among the three studied time series forecasting models. Comparative study shows that HW and SARIMA are superior models than GM (1, 1) for forecasting seasonal time series under TR, MMR and MDA criteria. In addition, the authors of this study showed that GM (1, 1) grey model is unqualified for seasonal time series data.

Rough Set Theory and its Applications: A recent Survey

Rough set theory is a mathematical method proposed by Pawlak . Rough set theory has been developed to manage uncertainties in information that presents missing and noises. Rough set theory is an expansion of the conventional set theory that supports approximations in decision making process. Fundamental of assumption of rough set theory is that with every object of the universe has some information associated it. Rough set theory is correlate two crisp sets, called lower and upper approximation. The lower approximation of a set consists of all elements that surely belong to the set, and the upper approximation of the set constitutes of all elements that possibly belong to the set. The boundary region of the set consists of all elements that cannot be classified uniquely as belonging to the set or as belonging to its complement, with respect to the available knowledge Rough sets are applied in several domains, such as, pattern recognition, medicine, finance, intelligent agents, telecommunication, control theory ,vibration analysis, conflict resolution, image analysis, process industry, marketing, banking risk assessment etc. This paper gives detail survey of rough set theory and its properties and various applications of rough set theory.