Rough Set Approach Research Articles

A parallel rule-based approach to compute rough approximations of dominance based rough set theory

In many datasets, conditional attributes and decision classes are preference-ordered, however, the classical Rough Set Theory (RST) does not consider the preference order between the values of the attributes. An extension of RST known as a Dominance-based Rough Set Approach (DRSA) provides dominance relation in this regard. In DRSA, data analysis mainly depends on the calculations of lower and upper approximations and these two measures are computationally utilizing many resources i.e., time and memory, due to the consideration of preference order. In this paper, we have proposed a parallel technique for calculating DRSA approximation sets. The proposed method directly computes approximations by following heuristic rules without calculating dominance positive or negative relations. The proposed parallel approach is then compared with the conventional method of calculation of DRSA approximations and a recent another technique of parallel processing using ten UCI publicly available datasets. Results validated the efficiency and effectiveness of the proposed model. An average reduction of 83% was observed in execution time and 86% in memory consumption. The structural complexity of the algorithm also considerably reduced.

The Dominance-based Rough Set Approach for analysing patterns of flexibility allocation and design-cost criteria in large-scale irrigation systems

Water planners must provide end-users with reliable and high-quality access to fresh water while complying with financial, institutional, and water availability constraints. In the pursuit of these goals, an over-investment in design can result in stranded assets of significant value and often unwanted environmental implications. Under-investment can lead to supply restrictions affecting human health, the economy, and the environment. The present study uses the Dominance-based Rough Set Approach (DRSA) to develop a balancing strategy concerning complexities encountered in water resource planning for irrigation systems. The methodology relies on the Dominance-based Learning from Examples Module (DOMLEM) algorithm, which extracts minimal set of rules regarding relevant combinations between flexibility allocation and design-cost criteria. The algorithm delineates outcomes in the form of “if., then.” rules that translate decision possibilities facing water planners into: “if (the design is more flexible by this amount), then (we expect this range of cost increment”). Then, a confusion matrix is computed for each irrigation system in order to exclude the rules generating incorrect and ambiguous classification results. The outcome reveals that cost is more subject to elasticity at the hydrant (eh) increment than the network’s coefficient (r). Furthermore, the analysis reveals that the parameter P(q) has only a minor impact on the cost and, as a result, the final decision. Any elasticity (eh) less than 3 assigned to any given coefficient (r) becomes a low-cost increment. For any given value of (eh), the cost increases as the coefficient (r) decrease. Elasticity from 4 to 5 with a network's coefficient (r) equal to or greater than 18/24 becomes a medium-cost increment. Elasticity (eh) from 5 to 6 associated with an (r) equal or less than 16/24 becomes a very high-cost increment. Finally, rather than identifying one solution that seems better than others, this approach provides an interactive schematic that helps identify the appropriate range of flexibility justified by the expense criterion, which allows for debate and supports decision-making.

A probability estimation-based feature reduction and Bayesian rough set approach for intrusion detection in mobile ad-hoc network

A probability estimation-based feature reduction and Bayesian rough set approach for intrusion detection in mobile ad-hoc network

A Novel Method for Decision Making by Double-Quantitative Rough Sets in Hesitant Fuzzy Systems

In some complex decision-making issues such as economy, management, and social development, decision makers are often hesitant to reach a consensus on the decision-making results due to different goals. How to reduce the influence of decision makers’ subjective arbitrariness on decision results is an inevitable task in decision analysis. Following the principle of improving the fault-tolerance capability, this paper firstly proposes the graded and the variable precision rough set approaches from a single-quantitative decision-making view in a hesitant fuzzy environment (HFEn). Moreover, in order to improve the excessive overlap caused by the high concentration of single quantization, we propose two kinds of double-quantitative decision-making methods by cross-considering relative quantitative information and absolute quantitative information. The proposal of this method not only solves the fuzzy system problem of people’s hesitation in the decision-making process, but also greatly enhances the fault-tolerant ability of the model in application. Finally, we further compare the approximation process and decision results of the single-quantitative models and the double-quantitative models, and explore some basic properties and corresponding decision rules of these models. Meanwhile, we introduce a practical example of housing purchase to expound and verify these theories, which shows that the application value of these theories is impressive.

A noise-aware fuzzy rough set approach for feature selection

Feature selection has aroused extensive attention and aims at selecting features that are highly relevant to classification from raw datasets to improve the performance of a learning model. Fuzzy rough set theory is a powerful mathematical method for feature selection. The classical fuzzy rough set model is very sensitive to the noise while the noise samples in classification data often appear. In addition, fuzzy rough set theory does not fit well when the density distribution of the samples in the dataset varies greatly. Thus, it is of great significance to improve the robustness of fuzzy rough set models and its adaptability to data for feature selection. Inspired by these issues, we focus on the robust fuzzy rough set approach for feature selection. We first propose a robust fuzzy rough set model based on data distribution to achieve the purpose of anti-noise i.e., Noise-aware Fuzzy Rough Sets (NFRS) model. This model proposes a novel search mechanism, which weakens the sensitivity of the approximation operator to noise by considering the distribution of samples in the decision classes to weight the samples, further obtains three kinds of samples, i.e., intra-class samples, boundary samples, and outlier samples. Then, the degrees of relevance of the feature for class is defined by the dependency function based on the NFRS model to evaluate the significance of the feature subset. On this basis, an evaluation function about feature significance is constructed, which simultaneously considers the relevance and redundancy of a candidate feature provided for the selected subset and the remaining feature subset. A novel forward greedy search algorithm is presented to select a feature sequence. The selected features are subsequently evaluated with downstream classification tasks. Experimental using real-world datasets demonstrate the effectiveness of the proposed model and its superiority against comparison baseline methods.

Predicting Consumer Recommendation Decisions from Online Reviews: A Rough Set Approach

Predicting Consumer Recommendation Decisions from Online Reviews: A Rough Set Approach

A Model to Support Sustainable Resource Management in the “Etna River Valleys” Biosphere Reserve: The Dominance-Based Rough Set Approach

For several decades, studies and conventions have highlighted the importance of the ecosystem services provided by natural resources and biodiversity for humanity and the need to move their management towards a sustainable model. Inthe United Nations 2030 Agenda for Sustainable Development (2015), among its 17 Sustainable Development Goals (SDGs), there isalsoSDG 15, “Life on Land”, specifically dedicated to biodiversity, forests and desertification. The aim is to “sustainably manage forests, combat desertification, halt and reverse land degradation, and halt the loss of bio-diversity”. The UNESCO “MAB—Man and Biosphere” Programme could achieve this goal by establishing Biosphere Reserves (BRs). Among them, the establishment of the BR “Etna river valleys” is an opportunity to adopt sustainable development models in the “Etna Park system” (UNESCO site since 2013) for the regeneration of virtuous relationships between cities and rural areas and the promotion of the conservation of natural resources and biodiversity through strategies of territorial enhancement with a multifunctional vision of agriculture and territory. This research aims to provide, through the methodology of “Rough Sets”, a useful tool to support the strategic choices that stakeholders will be called upon to make for the management of the complex environmental, cultural and economic mosaic that characterises the BR “Etna river valleys”.The results show both weaknesses and strengths in the transition towards a sustainable land management model, which will nonethelesshave to be supported and accompanied by information and financial support.

Linear Diophantine Fuzzy Rough Sets: A New Rough Set Approach with Decision Making

In this article, a new hybrid model named linear Diophantine fuzzy rough set (LDFRS) is proposed to magnify the notion of rough set (RS) and linear Diophantine fuzzy set (LDFS). Concerning the proposed model of LDFRS, it is more efficient to discuss the fuzziness and roughness in terms of linear Diophantine fuzzy approximation spaces (LDFA spaces); it plays a vital role in information analysis, data analysis, and computational intelligence. The concept of (<p,p′>,<q,q′>)-indiscernibility of a linear Diophantine fuzzy relation (LDF relation) is used for the construction of an LDFRS. Certain properties of LDFA spaces are explored and related results are developed. Moreover, a decision-making technique is developed for modeling uncertainties in decision-making (DM) problems and a practical application of fuzziness and roughness of the proposed model is established for medical diagnosis.

Multi-Objective Optimization of Customer-Centered Intermodal Freight Routing Problem Based on the Combination of DRSA and NSGA-III

The satisfaction of requirements and preferences of shippers is critical to enable the practicability of solutions that are derived from intermodal transportation routing problems. This study aims to propose a decision process to help shippers participate better in routing decisions. First, we considered shippers’ requests on transportation cost, timeliness, reliability, and flexibility to construct a multi-objective optimization model. Then, to solve the interactive optimization method that was proposed, NSGA-III was applied to obtain the Pareto front and dominance-based rough set approach to model the preference information. Finally, a case study was conducted and an expert was invited as decision-maker to demonstrate the applicability of the proposed model and the effectiveness of the interactive method for shippers. The results are expected to provide shippers with more rational transportation schemes and insights for the sustainable development of intermodal transportation.

Multiobjective decision making strategy for selective albumin extraction from a rapeseed cold-pressed meal based on Rough Set approach

This article proposes the application of multiobjective decision making strategy for selective albumin extraction by Pareto’s domain determination and ranking by Decision Engineering tool based on the Rough Set approach. In this study, both competing process performance indicators of rapeseed albumin extraction and quality of the solid residue remaining from this process were considered for an overall valorization of the rapeseed cold-pressed meal. The impact of extraction pH and NaCl concentration on three process performance indicators including the albumin extraction yield, the albumin content in the extract, and the phytic acid content in the remaining solid residue was studied. The results of the optimization approach showed that the high selectivity of rapeseed albumin extraction was to the detriment of the productivity of the process and the quality of the solid residue. The optimal conditions for selective rapeseed albumin extraction while producing a high-quality solid residue were identified at pH 2.0 and 0.11molL−1 of NaCl concentration. Upon these conditions, good albumin extraction yield was achieved (55.5%) with a high selectivity (97.9% of albumins in the extract). The isolate obtained after purification exhibited suitable color, high solubility, promising emulsifying properties and good foaming properties, which is of interest for food applications. Besides, the identifying extraction conditions yielded a protein-rich solid residue (35.6% on dry matter basis) with reduced phytic acid content (3.6% on dry matter basis), which can be used for feed applications.

A regret theory-based three-way decision approach with three strategies

Breast cancer is a malignant tumor that seriously threatens women’s health. Although classic multi-attribute decision-making (MADM) techniques can handle this kind of medical problem, a decision-maker (DM) can only collect sample data under various indicators for result ranking and analysis. The three-way decision (3WD) theory further supplements a classification scheme. In addition, a DM’s own limited rationality and personality traits have a strong impact on decision-making results. By using a dominance-based rough set approach (DRSA), a new 3WD method based on the regret theory (RT) with optimistic, neutral and pessimistic strategies (3WD-RT-OEP) is constructed in a fuzzy environment, so as to prevent diseases in advance and improve the survival rate of patients. First, attribute weights are calculated based on the similarity between the defined attributes of dominance classes, and a new method for calculating conditional probabilities is proposed to enhance the objectivity of the method by the similarity between object dominance classes and decision classes. Second, the score functions of three strategies are proposed by considering regret and rejoicing values in RT, and specific steps and algorithms of the 3WD-RT-OEP method are given as well. Finally, the validity and rationality of the constructed method are proved by experimental analysis with the support of case studies and supplementary data sets.

Improved evolutionary-based feature selection technique using extension of knowledge based on the rough approximations

This paper establishes an innovative approach of rough set (RS) approximations, namely the extension of knowledge based on the rough approximation (EKRA), which generalizes the old concepts and gets preferable results by reducing the boundary regions. In contrast to the former RS methods that obtained upper and lower approximations by several methods for special cases of binary relations. In addition, to assess the applicability of this approach it is combined with LSHADE with semi-parameter adaptation combined with CMA-ES (LSHADE-SPACMA) as a feature selection method, where EKRA is used as an objective function. The developed FS approach, named, LSPEKRA, which depends on LSHADE-SPACMA and EKRA aims to find the relevant features. This leads to improving the classification of different datasets. The experimental results show the great performance of the presented method against other Evolutionary algorithms. In addition, the FS methods based on EKRA provide results better than traditional RS in terms of performance measures.

A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance

A fuzzy rough set approach to hierarchical feature selection based on Hausdorff distance

Redefining preliminaries of dominance-based rough set approach

Redefining preliminaries of dominance-based rough set approach

Neighborhood Rough Set Approach With Biometric Application

This paper provides a new approach for human identification based on Neighborhood Rough Set (NRS) algorithm with biometric application of ear recognition. The traditional rough set model can just be used to evaluate categorical features. The neighborhood model is used to evaluate both numerical and categorical features by assigning different thresholds for different classes of features. The feature vectors are obtained from ear image and ear matching process is performed. Actually, matching is a process of ear identification. The extracted features are matched with classes of ear images enrolled in the database. NRS algorithm is developed in this work for feature matching. A set of 20 persons are used for experimental analysis and each person is having six images. The experimental result illustrates the high accuracy of NRS approach when compared to other existing techniques.

Exploration of Pressure Injury Risk in Adult Inpatients: An Integrated Braden Scale and Rough Set Approach

Exploration of Pressure Injury Risk in Adult Inpatients: An Integrated Braden Scale and Rough Set Approach

Rough Sets Hybridization with Mayfly Optimization for燚imensionality燫eduction

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular basis. Dimensionality reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed easily. These tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning issues. To achieve dimensionality reduction for huge data sets, this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS. A novel hybrid strategy based on the Mayfly algorithm (MA) and the rough set (RS) is proposed in particular. The performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the literature. The simulation results and comparison with common reduction methods demonstrate the proposed MA-RS algorithm’s capacity to handle a wide range of data sets. Finally, the rough set approach, as well as the hybrid optimization techniques PSO-RS and MA-RS, were applied to deal with the massive data problem. MA-hybrid RS’s method beats other classic dimensionality reduction techniques, according to the experimental results and statistical testing studies.

Evaluation of importance for condition attributes based on quality of decision reducts

Relative or decision reducts belong with mechanisms dedicated to feature selection, and they are embedded in rough set approach to data processing. Algorithms for reduct construction typically aim at dimensionality reduction aspect, searching for smallest reducts, which are considered as the most advantageous from the point of view of knowledge representation. However, classifiers build on reduced data models, based on reducts, can significantly vary in performance. Therefore, to ensure quality of predictions, other characteristics of reducts, apart from their cardinalities, need to be taken into account. The paper presents research in which estimation of reduct quality through their characteristics was reflected in calculation of the proposed weighting factors leading to attribute rankings. These rankings were next employed in the process of filtering decision rules, inferred by classic rough set approach. Constructed rule-based classifiers were applied in the stylometric domain to solve a task of authorship attribution.

Student absenteeism in engineering college using rough set and data mining approach

Student absenteeism in engineering college using rough set and data mining approach

How Fair Were Covid-19 Restriction Decisions? A Data-Driven Investigation of England Using the Dominance-Based Rough Sets Approach

How Fair Were Covid-19 Restriction Decisions? A Data-Driven Investigation of England Using the Dominance-Based Rough Sets Approach