Dominance-based Rough Set Approach Research Articles

Dominance-based rough set approach for the detection of cyber attack

Dominance-based rough set approach for the detection of cyber attack

Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design

Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity.

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.

A New Application with Conditional Risk Matrix and Decision Rules: An Assessment of the Sustainability and Performance Electric Power Company

Abstract The 2030 Agenda for Sustainable Development of the United Nations General Assembly focuses on economic development, poverty cessation, environmental sustainability, and good governance at all levels, including peace and safety. For electric power distribution concessionary companies in Brazil, it is necessary to evaluate their sustainability and performance according to the parameters laid by the National Electric Power Agency (ANEEL). For that purpose, sixty-two indicators were assembled that were directly and indirectly associated with sustainability for 38 companies in the period–2013-2021. This study proposes a new application with a conditional risk matrix and decision rules aimed at the integrated assessment of sustainability and performance of electric power companies using the conditional probability of indicators in the ascent or descent of the companies’ classification. A set of “if..., then...” decision rules were inferred by the algorithm VC-DomLEM using concepts from the Rough Sets Theory and the Dominance principle, Dominance-based Rough Set Approach (DRSA).

Application of dominance-based rough set approach in vaginal dosage forms optimization

The objective of this study was to identify cause-effect relationships between various data related to vaginal dosage forms, such as composition, process parameters (condition attributes), and quality (decision attribute), by employing a mathematical data mining approach that utilizes Rough Set Theory. The analyzed data were organized in a tabular format known as an information system. Objects were labeled as rows, and attributes as columns, with attribute-values serving as entries. Each formulation was described by its condition attributes (composition, manufacturing process, quality parameters), while the decision attribute indicated the quality of the formulation (expressed as pH after vaginal tablet or pessary application) and enabled classification as correct/incorrect or good/bad. The decision rules generated by the study provide insights into relationships between the condition attributes and the decision attribute. They highlight the importance of LA content and LA:polymer ratios in maintaining physiological pH. Additionally, the findings confirm the favorable impact of chitosan on pH regulation while indicating that the type of formulation (pessaries or tablets) does not significantly affect the classification of objects. Decision rules with high confirmation coefficients of condition attributes can help developing optimal formulations. These decision rules contain essential information that describes the cause-and-effect relationships and are free of redundant information. They form the basis for understanding and optimizing processes, and establishing a design space.

Aggregating midpoint-indicator results from Environmental Product Declarations for comprehensive evaluations of products' profiles, through the Dominance-based Rough Set Approach: An application in the Durum-Wheat Pasta Sector

To-date there is an unsolved methodological problem for the type-III labelling scheme, namely the Environmental Product Declaration (EPD), that essentially derives from the midpoint approach used for the Life Cycle Assessment (LCA) which the EPD itself is based upon. As a matter of fact, in the current EPD system, it is impossible to perform univocal aggregated evaluations of the product's profile, due to the different equivalent unit-of-measures which the midpoint impacts are expressed with, and to the absence of a scientifically-based, objective scheme for aggregation and weighing.The authors wanted to undertake this challenge with the aim of enhancing the effectiveness and utility of EPDs to guide managers, consumers and other stakeholders in the promotion, distribution, and consumption of sustainable products, in the context of green public procurement. To achieve this goal for the case of durum-wheat pasta, the EPD was complemented by the authors with the Dominance-based Rough Set Approach (DRSA). In result, an aggregate preference model considering all impact categories was obtained, thereby leading to comprehensive comparisons of complete environmental profile of products. The model has the form of a set of minimal decision rules, that can be used by producers for understanding the environmental performance level of their pasta products, and subsequently making strategic decisions for its improvement.Seven of those rules were determined from DRSA application, that regarded grain production and milling, package manufacturing, and a comprehensive downstream phase that included the disposal of the packaging system, and the pasta cooking with water waste disposal. In addition to this, those rules primarily considered the following midpoint categories within the ensemble of those provided by the EPD system for pasta products: acidification, eutrophication, global warming, and mineral element depletion.All of them were certain rules: three of the “at least” type (two at least good, and one at least medium); and four of the “at most” type (one at most bad, and three at most medium).By referring the given PCR-based LCA results to the threshold values of performance on criteria present in the premise of these rules, producers can know in advance the quality class (bad, medium, or good) which their pasta products belong to.In case of a negative evaluation, they can try to improve the value of some crucial indicators before applying for obtaining the EPD, and so enhance the environmental profile of their products.The authors believe that their study advances knowledge and enriches the literature concerning the application of multiple criteria decision analysis (MCDA), wherein DRSA stands out as an outstanding example. This contribution is particularly significant in enhancing and valorising sustainability assessment tools and certification schemes.Under this perspective, the study documented that leveraging DRSA to supplement the EPD system, for the particular case of pasta products, can contribute directly and indirectly to achieving Sustainable Development Goals (SDGs) and, so, to advancing environmental sustainability within the food sector.

Acquisition of representative objects and attribute reductions based on generalized decisions of dominance-based rough set approach

Dominance principles play a pivotal role in decision makings, in which the superior the object performs on criteria, the higher the object will be assigned by decision attributes. In practical issues, the result of decision makers is influenced significantly because of the existence on redundant objects and attributes. However, little work studies the inconsistency of dominance principles caused by redundant objects. The proposed generalized decision aims to intuitively reveal the consistency of dominance principles. Since then, considerable attention has been devoted to accelerating attribute reductions in dominance-based rough sets. But there has been limited discussion on the semantic explanation of generalized decisions. This paper addresses this gap by introducing a graded information granule that not only explains the consistency of dominance principles but also facilitates the partitioning of the universe. Based on this foundation, several theoretical properties are thoroughly investigated. Moreover, we acquire a representative object set by applying a threshold to filter out objects with an extreme imbalance of dominance principles, and a final compact data set is generated with the best threshold by a particular parameter selection mechanism. Additionally, we design an attribute reduction method based on graded information granules using the compact data set. Furthermore, we employ numerical data sets to illustrate the effectiveness and efficiency of our method by running time and importance of reducts performed from the perspective of dominance-based rough sets and machine learning. Finally, the practicability of compact data sets is explained from the viewpoint of decision rules in dominance-based rough set approach.

Estimating relative importance of criteria by post-processing dominance-based rough set approach’s outputs

The dominance-based rough set approach (DRSA) is one of the rare multicriteria sorting methods that does not require a priori values for the relative importance of criteria. This method therefore is cognitively less demanding. However, the decision-maker may lack some valuable information that is needed to explain and justify final decisions to stakeholders. The relative importance of criteria can instead be estimated based on the characteristics of the outputs of the DRSA, which are mainly decision rules and attribute reducts. The estimated values can then be used to understand the role played by each criterion and justify the final decisions to stakeholders. This paper first reviews and improves the decision rule attractiveness measure and the relative importance measures that are based on decision rules and attribute reducts, which were all proposed in the authors’ previous work. It also introduces new measures that rely on the marginal contributions and entropy of the criteria. This paper also extends these measures to estimate the relative importance of criteria with respect to specific unions of classes. The proposed measures have been applied to Brexit referendum and bankruptcy risk assessment datasets. Both applications showed that the proposed measures generally lead to different results. This can be explained by the diversity of the knowledge on which these measures rely and also by the multiplicity of the analytical perspectives associated with them. The results also showed that the marginal contribution-based measure is a good estimator of both Shapley and Banzhaf values.

Understanding green procurement dynamics: An assessment framework for public sector organizations

Governments globally face increasing pressure from climate advocates and international agreements, such as the Paris Agreement, to enact policies addressing climate change. This paper addresses the imperative for sustainable practices outlined in such agreements, with a specific focus on assessing the drivers of Green Procurement Practices (GPP) within Public Sector Organizations (PSOs). A dearth of research exists in systematically analyzing and prioritizing these drivers, exploring their interdependencies, and elucidating their relative importance. GPP is pivotal in market transformation by promoting environmentally friendly products and endorsing low-carbon, energy-efficient alternatives. This, in turn, contributes significantly to mitigating climate change and fostering a shift towards a greener, more sustainable economy. Identification of the drivers has been performed by an extensive review of the literature combined with the author's viewpoint, while the analysis has been performed using the novel method of Dominance-based Rough Set Approach (DRSA) and Interpretive Structural Modelling (ISM) with Matriced' Impacts Croise's Multiplication Applique'e a UN Classement (MICMAC) analysis. The study's outcome reveals that the Demand for Eco-friendly products is the primary driver for the incorporation of GPP, followed by the drivers' Presence of guidelines support and Government Regulations. Findings of the research also demonstrate that suppliers' propensity to adopt green practices depends on several factors, including sustainable supplier cooperation, degree of commitment to embrace green initiatives, government interventions in the form of incentives and guidelines support, and the presence of a legal framework. The findings of this research will enrich the understanding of policymakers and managers to formulate strategies for advancing GPP structured and sustainable implementation in PSOs. The study's findings will also benefit green technology sector advancement through the widespread adoption of GPP.

An approach to investigate fairness using Dominance-based Rough Sets Analysis—How fair were the COVID-19 restriction decisions in the UK?

Fairness is a crucial aspect to consider within decision support systems, to seek to strive for equitable decision outcomes. Therefore, in this work, we introduce an approach to investigate fairness in data-driven decisions. The Dominance-based Rough Sets Approach (DRSA) has been widely used to extract a single set of if-then types of rules from data. Conversely, our approach investigates fairness by extracting multiple separate if-then rule sets for separate groups. The proposed approach facilitates fairness analysis to be performed amongst groups represented by these rule-sets.During the COVID-19 pandemic, several countries have taken the approach of tiered restrictions, which has remained a point of debate due to a lack of transparency. Using our proposed approach, we explore fairness analysis with regards to the UK government’s COVID-19 tiered restrictions allocation system. These insights from the analysis are translated into “if-then” type rules, which can easily be interpreted by policy makers. The differences in the rules extracted from different geographical areas suggest inconsistencies in the allocations of tiers in these areas. We found that the differences delineated an overall north south divide in England, however, this divide was driven mostly by London. Such analysis could provide a more transparent approach to localised public health restrictions, which can help ensure greater conformity to the public safety rules. Our analysis demonstrates the usefulness of our approach, to explore fairness analysis in terms of equal-treatment within data-driven decisions, which could be applied in numerous other domains, for investigating the fairness and explainabilty of decisions.

Fast calculation for approximations in Dominance-based Rough Set Approach using Dual Information Granule

The Dominance-based Rough Set Approach (DRSA) is an extension of RST, which utilizes the dominance relation in attributes. However, traditional DRSA-based methods do not exploit the properties and logical structure in depth, which causes a high computational cost in lower and upper approximations. Besides, these methods are object-based, leading to repeated calculations from identical instances, and they involve numerous redundant computations to approximate different decisions. Therefore, we propose a novel approach, called DIGAC (Dual Information Granule-based Approximation Calculation), to improve DRSA by replacing the object-based calculation with the granule-based calculation. It effectively reduces the time complexity by constructing a granule-based ordered decision system. Additionally, this novel approach uses three types of Dual Information Granules (DIGs) to avoid repeated calculations from identical samples. In the process of lower and upper approximation, we leverage their transitivity to put forward a Distributed Storage-based Lower Approximation Calculation (DSLAC) strategy and a Query-based Upper Approximation Calculation (QUAC) strategy to eliminate redundant computations. Importantly, we theoretically prove that the DIG-based approach extensively reduces the time complexity of the approximations and obtains the same approximations as the original counterpart. Our approach is investigated on 23 datasets, and the experimental results show that it outperforms existing algorithms in terms of efficiency and stability, especially for large-scale and high-dimensional datasets, where the average decrease in execution time is up to 99%.

A computationally efficient approximation calculation method in dominance-based rough set approach

Recently Dominance-based Rough Set Approach (DRSA) has become a prominent tool for processing the datasets with preference order. However, DSRA requires calculating lower and upper approximations, which are computationally expensive. We have proposed a computationally efficient approximation calculation method that traverses each item in the dataset and updates the lower and upper approximations according to their preference order. In this way, there is no need to traverse the dataset multiple times, which not only enhances the performance but also produces the same results as produced by the conventional approach. Results on ten benchmark datasets from UCI have shown that the proposed approach significantly reduces the average computation time, that is, 19.5% in the case of lower approximation and 11% in the case of upper approximation. Memory consumption is also reduced by 99%. This clearly shows the efficiency and effectiveness of the proposed approach.

Explaining and predicting customer churn by monotonic rules induced from ordinal data

In the course of a computational experiment on bank customer churn data, we demonstrate the explanatory and predictive capacity of monotonic decision rules. The data exhibit a partially ordinal character, as certain attribute value sets describing the clients are ordered and demonstrate a monotonic relationship with churn or non-churn outcomes. The data are structured by the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) prior to the induction of monotonic decision rules. The supervised learning is conducted using an extended version of VC-DRSA, implemented in RuLeStudio and RuleVisualization programs. The first one is designed to experiment with parameterized rule models, and the second one is used for visualization and a thorough examination of the rule model. The monotonic decision rules give insight into the bank data, characterizing loyal customers and the ones who left the bank. Such an approach is in line with explainable AI, aiming to obtain a transparent decision model, that can be easily understood by decision-makers. We also compare the predictive performance of monotonic rules with some well-known machine learning models.

Interval Dominance-Based Feature Selection for Interval-Valued Ordered Data.

Dominance-based rough approximation discovers inconsistencies from ordered criteria and satisfies the requirement of the dominance principle between single-valued domains of condition attributes and decision classes. When the ordered decision system (ODS) is no longer single-valued, how to utilize the dominance principle to deal with multivalued ordered data is a promising research direction, and it is the most challenging step to design a feature selection algorithm in interval-valued ODS (IV-ODS). In this article, we first present novel thresholds of interval dominance degree (IDD) and interval overlap degree (IOD) between interval values to make the dominance principle applicable to an IV-ODS, and then, the interval-valued dominance relation in the IV-ODS is constructed by utilizing the above two developed parameters. Based on the proposed interval-valued dominance relation, the interval-valued dominance-based rough set approach (IV-DRSA) and their corresponding properties are investigated. Moreover, the interval dominance-based feature selection rules based on IV-DRSA are provided, and the relevant algorithms for deriving the interval-valued dominance relation and the feature selection methods are established in IV-ODS. To illustrate the effectiveness of the parameters variation on feature selection rules, experimental evaluation is performed using 12 datasets coming from the University of California-Irvine (UCI) repository.

Explainable interactive evolutionary multiobjective optimization

We present a new approach to Interactive Evolutionary Multiobjective Optimization (IEMO) guided by a preference elicitation procedure inspired by artificial intelligence and designed in line with decision psychology. For a conflicting nature of objectives, a solution optimizing all objectives simultaneously does not exist and thus the best compromise solution is searched for. In the interactive search, preference elicitation phases alternate with optimization phases. In the IEMO procedure proposed in this paper, during the preference elicitation phase, the Decision Maker (DM) gets a sample of solutions from the current population and is asked to indicate relatively good solutions in this sample. Using the Dominance-based Rough Set Approach (DRSA), this information is summarized by “if ..., then ... ” decision rules which represent DM’s preferences. They are used in the next optimization phases of the IEMO to influence the crossover so as to converge towards the part of the Pareto front containing the best compromise solution. Besides guiding the search process, the decision rules can be read as arguments explaining the DM’s preferences. In this way, the proposed method implements the postulate of transparency and explainability expected from interactive procedures. The DM has a chance to understand how their answers given in the preference elicitation phase are translated into guidelines for the algorithm in the optimization phase. This is a distinctive aspect of what we call eXplainable Interactive Multiobjective Evolutionary optimization Approach (XIMEA). The presented XIMEA-DRSA method implements this approach. From the viewpoint of behavioral psychology, the decision rules support the DM to construct and learn their preferences in the course of evolutionary optimization. To check the efficiency of XIMEA-DRSA, we performed several experiments on continuous as well as combinatorial test problems, assuming the existence of an artificial DM that iteratively provides its preferences according to a known value function. The results prove that XIMEA-DRSA is converging to the most interesting part of the Pareto front, similar to an evolutionary algorithm that optimizes the value function of the artificial DM and, therefore, the latter is used as a benchmark.

Criteria Selection of Housing Loan Based on Dominance-Based Rough Set Theory: An Indian Case

Because India has one of the world’s fastest-growing economies, the Indian banking sector is essential to the country’s reform. The approval of home loans to customers is one of the crucial tasks carried out by Indian banks. The risk of loan repayment outside of the agreed-upon time frame can be reduced by accurately estimating the customer’s loan need. The majority of earlier studies on the development of banking lacked a methodical approach to analyze qualitative data. Even though the traditional multivariate statistically based factor analysis approach is a great way to categories data in qualitative analysis, the technique cannot be used without any statistical presumptions and additional information about the data. This study handles the banking attributes related to home loans using the Dominance-based Rough Set Approach (D-RSA). In order to categorize the customer’s attributes, this study suggests using a preference-based “if … then” decision rule. This rule can aid decision makers in understanding the risk factors associated with loan factors for a financial organization.

EVALUATION OF MODERN HERITAGE ASSETS IN POZNAN USING MONOTONIC DECISION RULES

Abstract. The protection of cultural heritage is an important task of communities on various levels of social organization. The institutionalization of the processes of protection of modern heritage assets provides the necessary instruments (legislative, juridical, financial) enabling the actual realization of the assumed tasks. The criterion of age, which is still a dominating premise for monument protection, proved not to be sufficient, especially concerning protection of monuments of Modernism. A step that led to the determination of the value of individual architectural objects of the 20th century was the establishment of 10 evaluation criteria proposed by historians of architecture in Warsaw, and afterwards in Poznan.In this work, we focus on the architectural value of post-war buildings, which are most difficult to evaluate. Furthermore, we wanted to apply AI to objectify the process of decision making. The adequacy of the Dominance-based Rough Set Approach (DRSA) method has been established. This method takes into account preference orders on criteria and models patterns observed in data in terms of monotonic “if …, then …” decision rules.

Fuzzy rough feature selection using a robust non-linear vague quantifier for ordinal classification

Ordinal classification is a common classification problem, which widely exists in multi-attribute decision making problems. The dominance-based rough set approach (DRSA) is a knowledge acquisition tool for ordinal classification tasks. Nevertheless, the existence of noise information in the collected data greatly reduces the accuracy of the DRSA and some extended model. An effective way to solve this issue is to improve the robustness of these models. In addition, most existing feature selection strategies based on dominance rough sets mainly focus on the monotonic classification consistency between features and decision, but ignoring the classification information provided by features combination. Motivated by these two issues, this paper proposes a robust fuzzy dominance rough set model to combat noise interference and develop a feature selection approach based on the proposed model for ordinal classification tasks. First, a non-linear vague quantifier is adopted to construct the robust model, and related dependency function is introduced. Second, the rank entropy based uncertainty measures are explored to characterize the contribution of features combination for ordinal classification. Based on the proposed uncertainty measures, a new feature evaluation index is presented. Meanwhile, the corresponding feature selection algorithm is designed. Finally, numerical comparative experiments are performed to indicate that the proposed model and feature selection algorithm have good capability.

Sustainable supplier selection in a construction company: a new MCDM method based on dominance-based rough set analysis

Purpose This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions. Design/methodology/approach This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool. Findings The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process. Originality/value The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

A data-driven decision support system for sustainable supplier evaluation in the Industry 5.0 era: A case study for medical equipment manufacturing

Effective supplier management is critical for an enterprise’s success, as supplier procurement accounts for up to approximately 70% to 80% of total manufacturing costs. Correct supplier selection can ensure the competitiveness of the enterprise and contribute to supply chain integration and innovation. In recent years, supplier evaluation frameworks based on Industry 4.0 concepts have contributed to the development of the industry. However, the novel concepts of Industry 5.0 require examination from a people-oriented and sustainable perspective. Unfortunately, at the present time, supplier evaluation frameworks based on Industry 5.0 are lacking. Therefore, the primary task of this study is to develop a novel and comprehensive supplier evaluation framework for the Industry 5.0 era. This study proposes a data-driven decision support system to execute the supplier evaluation process. First, variable precision- dominance-based rough set approach (VC-DRSA) is applied to extract the core criteria, to remove the noise factors and to generate decision rules for the decision-makers’ reference. Second, the criterion importance through intercriteria correlation (CRITIC) approach is adopted to obtain the dependency weights of the core criteria and their ranking. Finally, a modified classifiable technique for order preference by similarity to ideal solution (CTOPSIS) is used to integrate the final performance values of suppliers when new alternative suppliers are added. The research concept is in line with the conception of data-driven decision support in business intelligence and does not rely on the subjective judgments and opinions of experts. Data provided by a multinational medical equipment manufacturer are used as an example to demonstrate the proposed model. VC-DRSA retains nine core criteria from the original twenty criteria, which greatly reduces the labor and cost of supplier audits. In addition, the CRITIC results show that digital transformation, real-time information sharing, and organizational culture transformation are the three main factors affecting the development of enterprises towards Industry 5.0. The results show that CTOPSIS can be used to quickly assess the ratings of new alternative suppliers are listed.