Rough Set Approach Research Articles

Weighting factor for attributes based on reduct cardinality

Estimation of attribute importance can be obtained by a mechanism that allows to assign some weight to variables. Weighting attributes can lead to their ordering, which, in turn, can be exploited for feature selection and reduction. Decision reducts constitute an example of a mechanism aiming at dimensionality reduction, embedded in rough set approach to data mining. The paper presents research works, where the process of weighting was driven by the proposed factor based on reducts, with varying their sets, and the results were analysed through the perspective of reduct cardinality, since it is typically considered as the most significant indicator of reduct quality. Constructed rankings of variables were used for inferring sets of decision rules from gradually decreasing numbers of features, and then the performance of the rule classifiers was tested. The experiments show that for the weighting factor to be useful for feature reduction, not only reduct cardinalities, but also the numbers of reducts found need to be taken into account.

Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms.

Assessment of decision-making method

The method presented in Multi Criteria Decision Aiding (MCDA) focuses on the formulation of choice and is expressed by decision-making law "if…then…. " Basic premise facts as well as the quest for clear rules to encourage its judgements. This approach has a significant benefit in handling contradictions as a consequence of hesitations. The method suggested is based on the fundamental, natural and logical concept of dominance. It states something else in a particular family, and then it in general, according to all requirements. The gathering of judgements based on preferred facts using an information discovery method adapted to reflect the dominance principle. In order to demonstrate how Dominance Rough Set Approach (DRSA) may be used clearly in various MCDA situations, the authors’ Dominances-based Rough Set Methodius acts as a mathematical basis for the MCDA decision-making approach, along with teaching examples.

Knowledge-based mining with the game-theoretic rough set approach to handling inconsistent healthcare data

Knowledge-based mining with the game-theoretic rough set approach to handling inconsistent healthcare data

Explainable Interactive Evolutionary Multiobjective Optimization

We present a new interactive procedure for evolutionary multiobjective optimization involving the decision rule preference model in the search for the best compromise solution. As usual in the interactive multiobjective optimization, preference elicitation phases alternate with optimization phases, however, in this evolutionary procedure, one preference elicitation phase falls into many iterations of the evolutionary multiobjective optimization. In the preference elicitation phase, the Decision Maker (DM) is asked to select those solutions she considers relatively good in the current population. Using the Dominance-based Rough Set Approach (DRSA) this information is represented in terms of ``if ..., then ... decision rules which represent DM’s preferences. They are used in the next phases of the evolutionary multiobjective optimization to iteratively 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 choices. In this way, the proposed method implements the postulate of transparency and explainability expected from interactive procedures, because the DM has a chance to understand how her answers given in the preference elicitation phase are translated into guidelines for the algorithm in the optimization phase, i.e., what is the impact of the answers on newly proposed compromise solutions. This can be considered the distinctive aspect of what we define explainable multiobjective optimization evolutionary approach (XIMEA). In particular, the method proposed in this paper is called XIMEA-DRSA. From the decision psychology point of view, in line with the main results of behavioral sciences, the decision rules support the DM to construct and learn her preferences during the decision process. For its explanatory feature resulting from the conjunction of evolutionary multiobjective optimization with DRSA, the method has been called XIMEA-DRSA. The computational effectiveness of the method has been tested on both continuous and discrete multiobjective optimization problems. In the latter case, the XIMEA-DRSA method has been applied to the multiobjective knapsack problem.

Kansei evaluation for group of users: A data-driven approach using dominance-based rough sets

Kansei refers to people’s subjective feeling and impression. Kansei evaluation devotes to assessing users’ preferences for product items according to multiple Kansei attributes, thus supporting the decision making of consumers and/or designers. The objective of this paper is to propose a data-driven approach for addressing user group oriented Kansei evaluation. The approach consists of three phases. The first phase identifies the representative Kansei attributes and product samples of the product domain to gather exemplary evaluation dataset from sampled representative users. In light of the specified Kansei need and relying on the dominance-based rough set approach, the second phase constructs the collective decision table so as to further infer the collective preferential information in terms of dominance-based decision rules and Kansei importance weights. The third phase presents a two-step sequential heuristic model for characterizing users’ affective preference behavior: (1) a multicriteria classifier using dominance-based decision rules for product sorting, partly simulating the satisficing heuristic; and (2) a simple choice strategy for product ranking, manifesting the CONF heuristic. A case study involving the toaster domain was conducted to verify the proposed approach. The theoretical and practical implications of the proposed approach are also discussed.

Fuzzy extensions of the dominance-based rough set approach

In this paper, we first review existing fuzzy extensions of the dominance-based rough set approach (DRSA), and advance the theory considering additional properties. Moreover, we examine the application of Ordered Weighted Average (OWA) operators to fuzzy DRSA. OWA operators have shown a lot of potential in handling outliers and noisy data in decision tables, when they are combined with the indiscernibility-based rough set approach (IRSA). We examine theoretical properties of the proposed hybridisation of OWA operators with fuzzy DRSA. At the end, we experimentally compare the robustness of the standard fuzzy DRSA approach with the OWA one.

Identification of Attack on Data Packets Using Rough Set Approach to Secure End to End Communication

Security has become one of the important factors for any network communication and transmission of data packets. An organization with an optimal security system can lead to a successful business and can earn huge profit on the business they are doing. Different network devices are linked to route, compute, monitor, and communicate various real-time developments. The hackers are trying to attack the network and want to draw the organization’s significant information for its own profits. During the communication, if an intrusion or eavesdropping occurs, it will lead to a severe disfigurement of the whole communication network, and the data will be controlled by wrong malicious users. Identification of attack is a way to identify the security violations and analyze the measures in a computer network. An identification system, which is effective and accurate, can add security to the existing system for secure and smooth communication among end to end nodes and can work efficiently in the identification of attack on data packets. The role of information security is to design and protect the entire data of networks and maintain its confidentiality, integrity, and availability for their right users. Therefore, there is a need for end to end security management, which will ensure the security and privacy of the network and will save the data inside networks from malicious users. As the network devices are growing, so the level of threats is also increasing for these devices. The proposed research is an endeavor toward the detection of data packets attack by using the rough set theory for a secure end to end communication. The experimental work was performed by the RSES tool. The accuracy of the K-NN was 88% for the total objects of 8459. For cross validation purposes, the decision rules and decomposition tree algorithms were used. The DR algorithm showed accuracy of 59.1%, while the DT showed accuracy of 61.5%. The experimental results of the proposed study show that the research is capable of detecting data packets attack.

Incremental attribute reduction approaches for ordered data with time-evolving objects

Dominance-based rough set approach (DRSA) is widely applied to multi-criteria decision analysis and sorting problems for data with preference-ordered relation, where attribute reduction is an important research field. At present, based on DRSA, many traditional attribute reduction approaches are extended to process a static ordered data. In real-world applications, ordered data with time-evolving objects widely exist, which is called a dynamic ordered data. However, for dynamic ordered data, employing these existing approaches to compute reducts is very time-consuming, since they need to recalculate knowledge from scratch when multiple objects vary. Incremental updating method can effectively complete the dynamic learning task, because it can acquire new knowledge based on previous knowledge. Inspired by this, this work studies incremental attribute reduction approaches for dynamic ordered data in DRSA framework. First, matrix-based method for calculating the dominance conditional entropy is investigated. Next, the updating principles of the dominance relation matrix and dominance diagonal matrix are studied when objects vary. Finally, two incremental algorithms of attribute reduction are proposed when multiple objects are added to or deleted from an ordered decision system, respectively. Experiments on different datasets provided by University of California at Irvine (UCI) are conducted to evaluate the proposed algorithms. Experimental results show that the proposed incremental algorithms can effectively and efficiently accomplish the task of attribute reduction in dynamic ordered data.

Application of Dominance-Based Rough Set Approach for Optimization of Pellets Tableting Process.

Multiple-unit pellet systems (MUPS) offer many advantages over conventional solid dosage forms both for the manufacturers and patients. Coated pellets can be efficiently compressed into MUPS in classic tableting process and enable controlled release of active pharmaceutical ingredient (APIs). For patients MUPS are divisible without affecting drug release and convenient to swallow. However, maintaining API release profile during the compression process can be a challenge. The aim of this work was to explore and discover relationships between data describing: composition, properties, process parameters (condition attributes) and quality (decision attribute, expressed as similarity factor f2) of MUPS containing pellets with verapamil hydrochloride as API, by applying a dominance-based rough ret approach (DRSA) mathematical data mining technique. DRSA generated decision rules representing cause–effect relationships between condition attributes and decision attribute. Similar API release profiles from pellets before and after tableting can be ensured by proper polymer coating (Eudragit® NE, absence of ethyl cellulose), compression force higher than 6 kN, microcrystalline cellulose (Avicel® 102) as excipient and tablet hardness ≥42.4 N. DRSA can be useful for analysis of complex technological data. Decision rules with high values of confirmation measures can help technologist in optimal formulation development.

Safety performance & evaluation framework in Indian construction industry

IntroductionThis paper proposes a framework to evaluate the safety performance of workers in Indian construction industry. The key indicators to measure the safety and health conditions are identified and prioritized on the basis of the importance in the establishment of safe and healthy work environment. Design/methodology/approachFrom the literature review and inputs from the construction managers, key safety indicators were identified. The prioritization was accomplished using multi-criteria ranking method (MCRM). Dominance-based Rough Set (DRSA) is used for the ranking of indicators. It is based on Rough set theory which draws motivation from the indiscernibility relation be- tween the objects. The importance of each indicator is assessed by MCRM method in the contribution towards establishing a safe workplace environ- ment from different perspectives in a construction industry that includes management from top, middle and low-level management. FindingsThe role and involvement of management in the operations and con- stant interaction with workers were found to have a significant impact on the safety performance of the firms. Imparting necessary safety training to the employees and promoting a friendly atmosphere at the workplace aids in building the confidence of the workers that subsequently make them safety compliant. Dedicated staff to ensure that the workers are following safety protocols enlisted in safety manuals acquires an important place in the measurement of the safety performance of the firms. Practical implicationsThe findings of the current study will help the senior management in the small scale Indian construction industry to gain a perspective to- wards the assessment of safety condition in the firms and establishing an efficient and easy-to-use safety assessment framework. It will also be useful for middle and low-level management, since they should read the results as a positive effect of their constant interaction with workers. Management is suggested to design a well-organized and detailed manual that docu- ments the safety-related information including the proper use of safety equipment and emergency procedures. Originality/valueThis paper proposes a safety framework for small-scale Indian construction industry for calibration and benchmarking safety performance of the employees. The mathematical foundation of the framework is the multi-criteria ranking based on Dominance based Rough set approach.

An optimized method to calculate approximations in Dominance based Rough Set Approach

Classical Rough Set Theory (RST) is a prominent tool to deal with uncertainty of categorical data. However, it does not consider the preference order between the values of the attributes. Dominance Based Rough Set Approach (DRSA) provides dominance relation in this regard. Computation of the upper and lower approximation is a critical step in DRSA. However, computing these approximations is a computationally expensive task. Efficiently computing approximations will thus be helpful in reducing the execution time of algorithms using these approximations. In this paper, we have proposed an efficient approach to compute these measures. The proposed approach directly calculates approximations without considering the objects that do not play any role in the approximations. In our approach, one instance of a dataset is compared with another instance only once which avoids unnecessary comparisons. The proposed approach is compared with the conventional method using sixteen benchmark data sets from UCI. Results show that the proposed approach significantly reduces the execution time. The average reduction in the execution time was found to be almost 85%. This approach also reduces the memory consumption by 75%. The Big-O complexity is also reduced. These measures justify that the proposed approach is more effective and efficient as compared to the conventional DRSA.

Automatic manpower allocation for public construction projects using a rough set enhanced neural network

Accurate estimates of manpower are still heavily dependent on well-experienced personnel. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh the impact attributes, and to develop a model to assess the total quantities of labor needed for a construction project using a rough set enhanced artificial neural network (ANN). Experts suggest 14 attributes that influence the estimation of on-site manpower for construction projects. After trimming and analyzing the basic data, the rough set approach is used to classify and weigh the attributes into three levels of impact based on their frequency. A rough set enhanced ANN is accordingly developed that yields an accuracy rate of 91.903%, higher than that of a regular ANN. A practical and effective prediction model benefits personnel having to estimate on-site manpower needs for construction projects.

Heuristic-based feature selection for rough set approach

The paper presents the proposed research methodology, dedicated to the application of greedy heuristics as a way of gathering information about available features. Discovered knowledge, represented in the form of generated decision rules, was employed to support feature selection and reduction process for induction of decision rules with classical rough set approach. Observations were executed over input data sets discretised by several methods. Experimental results show that elimination of less relevant attributes through the proposed methodology led to inferring rule sets with reduced cardinalities, while maintaining rule quality necessary for satisfactory classification.

Auto loan fraud detection using dominance-based rough set approach versus machine learning methods

Financial fraud is escalating as financial services and operations grow. Despite preventive actions and security measures deployed to mitigate financial fraud, fraudsters are learning and finding new ways to get around fraud prevention systems, thereby, challenging quantitative techniques and predictive models. Thus, new techniques must be explored and tested so the insights obtained from the analysis may be used to support more accurate fraud prediction and the development of fraud prevention systems which have additional checks to mitigate suspicious events. Auto loan is a significant financial product not yet explored in the literature, unlike the misuse of credit cards, for instance. Given the recent increase in fraudulent transactions concerning auto loan applications, this paper tests a new data set for auto loan applications using a technique not yet explored for financial fraud prediction, namely the Dominance-based Rough Set Balanced Rule Ensemble (DRSA-BRE), and after comparing it with other techniques traditionally used for predicting financial fraud, finds that the proposed approach has several advantages over the traditional ones.

Double-quantitative variable consistency dominance-based rough set approach

Rough set model with double quantification satisfies the requirement of quantitative information in practical applications, it has better fault tolerance than probabilistic rough set model considering only relative quantification and graded rough set model considering only absolute quantification. In this paper, two kinds of consistency levels are introduced from the perspective of double quantification in an ordered information system, namely relative quantitative consistency level and absolute quantitative consistency level. The single-quantitative variable consistency dominance-based rough set models based on these two kinds of quantitative consistency levels and their basic properties with the relevant three-way decision rules are discussed respectively in an ordered information system. Moreover, two kinds of double-quantitative variable consistency dominance-based rough set models and their basic properties with the relevant decision rules based on these two kinds of quantitative consistency levels are introduced. A consistency analysis of decision making in a practical case study is used to illustrate and interpret the double-quantitative variable consistency rough set models and the related decision rules in the ordered information system. The obvious shortcomings of dominance-based rough set approach (DRSA) without quantitative information are compared to explain the advantages of the quantitative variable consistency dominance-based rough sets with the two consistency levels in the practical case study.

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk.

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

Targeting Poverty and Developing Sustainable Development Objectives for the United Nation’s Countries using a Systematic Approach Combining DRSA and Multiple Linear Regressions

The objectives of this article is to target poverty using Dominance-based Rough Set Approach (DRSA) to help the United Nation’s Countries develop objectives for sustainable development. There are 12 variables divided into 2 perspectives. The first is an economical and technological perspective composed of 6 variables. The second is a sociological and political perspective composed of 6 variables. The methodology proposed classifies all the United Nation’s countries according to three different categories: [A] Developed countries; [B] Emerging economies that need support to acquire category A status; [C] Under Developed countries ranked the lowest and needing special support with regard to the criterion or criteria considered. Using this classification, DRSA provides decision rules to explain the classification and indicating precisely what are the conditions to be part of a higher category. Also, the results indicate what are the conditions to be part of the Under Developed countries category and therefore helps targeting poverty and proposing, at the same time, objectives to improve this classification. Finally, we used Multiple Linear Regressions with selected decision rules to test selected decision rules as the Gross National Income per capita as the dependent variable.

Toward an Effective Locality-Sensitive Hashing Search for WMSNs Based on the Neighborhood Rough Set Approach

With the advent of the 5G era, wireless multimedia sensor networks (WMSNs) will be more widely used in security monitoring, environmental monitoring, intelligent transportation, and other applications of the Internet of Things (IoT). Data collected by WMSNs, part of IoT systems, are high-dimensional, multilevel, unstructured, and large-scale complex data. Effective nearest neighbor searching faces the “dimension curse” problem. To this end, by incorporating the neighborhood rough set (NRS) approach, this article proposes a novel and effective locality-sensitive hashing (LSH) method for high-dimensional WMSN data based on the neighborhood (NLSH). This method innovatively combines the neighborhood knowledge representation method with the LSH mechanism and extends the indexing ability of LSH. Moreover, this method does not require any prior knowledge and has good universality. The indexing method of neighborhood bucket building can reduce the number of searches and improve the response speed of the WMSN system. Extensive experiments have been carried out on several real-world multimedia data sets. The results show that a large-scale high-dimensional NLSH search based on the NRS approach can efficiently query the target point and produce very accurate results. The NLSH algorithm based on the NRS approach outperforms other advanced mainstream LSH algorithms.

Understanding the drivers of Urban Development Agreements with the rough set approach and robust decision rules

The rise of neoliberalism in the context of urban development has encouraged cooperation between public and private parties. This cooperation is structured by contracts, generally called Urban Development Agreements (UDAs). Being part of the urban regeneration strategies, UDAs aim at achieving durable improvements of abandoned areas, namely brownfields, according to the sustainability principles. Thus, within the negotiation between private developers and public administrations, multiple and conflicting instances have to be faced case by case. Despite the uniqueness of each UDA, it is possible to define a set of pertinent characteristics that play a crucial role in determining the fairness and appropriateness of the public-private partnership. Given this context, we propose a novel variant of the Dominance Rough Set Approach (DRSA) for i) exploring the relationship between condition attributes or criteria and effects of urban development processes and for ii) supporting negotiations according to the detection of a set of relevant features. Specifically, DRSA is applied on a sample of UDAs recently concluded in the Lombardy Region (Northern Italy), and then tested on a sample of other UDAs under the negotiation phase. The analysis involves five dimensions represented by attributes and criteria related to urban, institutional, negotiation, development and economic contexts. The inferred decision rules provide useful knowledge for supporting complex decision processes such as the allocation of costs and benefits within UDAs.