Rough Set Approach Research Articles

Optimization deep learning with rough set approach model classification Otitis

<span>Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.</span>

Exploration of pressure injury risk in adult inpatients: An integrated Braden scale and rough set approach

ObjectiveThis study aimed to develop an interpretive model with decision rules to assess the risk level of pressure injuries in adult inpatients and identify the critical risk factors associated with these injuries. MethodsThe rough set approach was used to identify the critical risk factors associated with pressure injuries and demonstrate their behavioral patterns. The study focused on adult inpatients aged 18 or above who remained in bed for at least 24 hours after admission. The data was extracted from a nursing electronic medical record system of a hospital in Zhejiang Province, China, from 27 October 2019 to 1 November 2020. ResultsThe critical risk factors associated with pressure injuries in adult inpatients were identified as “Sensory perception,” “Nutrition,” and “Friction and shear.” A prediction model with 89 decision rules was established and demonstrated reliable predictive capabilities. Nursing staff should focus more on high-risk and severe-risk rules (Rules 11 to 18) to reduce the likelihood of potential high-risk pressure injuries. ConclusionsThe prediction model established by the rough set approach can be used to identify the critical risk factors of pressure injuries and has good explanatory ability, which can complement and improve the predictive accuracy of the Braden Scale. The decision-making rules can help nurses improve work efficiency. Implications for Clinical PracticeExplanatory analysis can explain most inpatients' potential risk patterns and corresponding critical risk factors. Data-driven research models and results can help nurses understand patients' potential risks better. Additionally, these insights can be valuable in nursing education, aiding new nurses in comprehending and addressing the potential risks patients face.

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.

A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

Intuitionistic fuzzy information is a potent tool for medical diagnosis applications as it can represent imprecise and uncertain data. However, making decisions based on this information can be challenging due to its inherent ambiguity. To overcome this, power aggregation operators can effectively combine various sources of information, including expert opinions and patient data, to arrive at a more accurate diagnosis. The timely and accurate diagnosis of medical conditions is crucial for determining the appropriate treatment plans and improving patient outcomes. In this paper, we developed a novel approach for the three-way decision model by utilizing decision-theoretic rough sets and power aggregation operators. The decision-theoretic rough set approach is essential in medical diagnosis as it can manage vague and uncertain data. The redesign of the model using interval-valued classes for intuitionistic fuzzy information further improved the accuracy of the diagnoses. The intuitionistic fuzzy power weighted average (IFPWA) and intuitionistic fuzzy power weighted geometric (IFPWG) aggregation operators are used to aggregate the attribute values of the information system. The established operators are used to combine information within the intuitionistic fuzzy information system. The outcomes of various alternatives are then transformed into interval-valued classes through discretization. Bayesian decision rules, incorporating expected loss factors, are subsequently generated based on this foundation. This approach helps in effectively combining various sources of information to arrive at more accurate diagnoses. The proposed approach is validated through a medical case study where the participants are classified into three different regions based on their symptoms. In conclusion, the decision-theoretic rough set approach, along with power aggregation operators, can effectively manage vague and uncertain information in medical diagnosis applications. The proposed approach can lead to timely and accurate diagnoses, thereby improving patient outcomes.

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.

Rough set approach to predict biochar stability and pH from pyrolysis conditions and feedstock characteristics

Biochar has gained increasing attention as a potential adsorbent for the sequestration of carbon dioxide (CO2) and enhancement of soil fertility. Biochar pH and stability are important adsorbent properties as they indicate the affinity of biochar for CO2 and its potential to be applied in soil for long periods. These attributes are influenced by the feedstock composition and pyrolysis conditions. Therefore, it is important to develop a model that can elucidate the underlying trends and inherent relationships between feedstock composition and pyrolysis operating parameters on biochar pH and stability. In this work, rough set machine learning (RSML) tools have been used to develop a model to quantify this relationship because of the interpretable nature of RSML. RSML is a rule-based prediction model that categorizes the biochar properties by utilizing ‘if. then’ rules to conditional attributes. In this study, the feedstock properties such as elemental (carbon, hydrogen, oxygen and nitrogen) composition, fixed carbon, ash content, volatile matter, and operating conditions such as residence time, heating rate, and temperature were the conditional attributes while pH, ash content and O/C ratio of biochar were considered the decision attributes. The rules generated from RSML were validated and evaluated for scientific coherency. Thus, this approach provided a model which could reflect on the physical phenomena at varying process conditions. As a result, it was recommended that the pyrolysis temperature is in between 375 and 475 °C, the ash content in the feedstock is between 2.59 and 3.55 wt% and volatile matter in the feedstock is in between 68.9 and 73.8 wt% to obtain biochar with minimum ash content (0–5 wt%), minimum O/C ratio (0–0.2), and high pH (9−11).

Sensor data reduction with novel local neighborhood information granularity and rough set approach

Data description and data reduction are important issues in sensors data acquisition and rough sets based models can be applied in sensors data acquisition. Data description by rough set theory relies on information granularity, approximation methods and attribute reduction. The distribution of actual data is complex and changeable. The current model lacks the ability to distinguish different data areas leading to decision-making errors. Based on the above, this paper proposes a neighborhood decision rough set based on justifiable granularity. Firstly, the rough affiliation of the data points in different cases is given separately according to the samples in the neighborhood. Secondly, the original labels are rectified using pseudo-labels obtained from the label noise data that has been found. The new judgment criteria are proposed based on justifiable granularity, and the optimal neighborhood radius is optimized by the particle swarm algorithm. Finally, attribute reduction is performed on the basis of risky decision cost. Complex data can be effectively handled by the method, as evidenced by the experimental results.

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.

SENE: A novel manifold learning approach for distracted driving analysis with spatio-temporal and driver praxeological features

Although there are many studies conducted on distracted driving, the growing number of accidents on roads demands further serious attention. The majority of the distracted driving-related data in real life are unlabeled and higher dimensional, leading to complex analyses. There is a lack of existence of proper indices for understanding the perilousness due to distracted driving, which makes it very difficult to understand which road or neighborhood has a higher risk of accidents. Despite earlier studies have focused on either spatiotemporal or praxeological factors separately, they have not considered both factors together. Moreover, crisp rule extraction and interpretation are lacking in the literature. Therefore, to deal with such issues, we have proposed a new methodology which: (i) develops Schrodinger Eigenmap Neighborhood Embedding (SENE) manifold learning for dimensionality reduction, followed by K-means clustering, (ii) develops road perilousness index (RPI) and neighborhood PI (NPI) to explain dangerousness of roads or neighborhoods, (iii) uses both spatiotemporal and driver praxeological factors, and (iv) develops Tolerance Rough Set Approach (TRSA) for crisp rules generation and interpretation. Road accident data from the Nevada Department of Transportation is used as a case study. Besides, a total of four benchmark datasets from the University of California Irvine repository are also used for comparative study to prove the superiority of our proposed methodology over some state-of-the-art. Experimental results reveal that the proposed methodology outperforms others by providing the highest clustering accuracy with four clusters. Finally, a set of 16 crisp rules are extracted and interpreted from clusters using TRSA.

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.

Manpower Allocation of Work Activities for Producing Precast Components: Empirical Study in Taiwan

The production of precast components in the construction industry is a labor-intensive process. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh impact attributes, and to develop a model to assess the total quantities of labor needed for precast structural elements using a rough set enhanced K-Nearest Neighbor (KNN). Three main building components (beams, girders, and columns) were collected from the production of precast elements in Taiwan. After trimming and analyzing the basic data, the rough set approach is used to classify and weight the attributes into three levels of impact based on their frequency. A rough set enhanced KNN is accordingly developed, yielding an accuracy rate of 92.36%, which is 8.09% higher than the result obtained when using the KNN algorithm. A practical and effective prediction model would assist managers to estimate the manpower requirement of precast projects.

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.

Semi-supervised attribute reduction for partially labelled multiset-valued data via a prediction label strategy

The existence of a large amount of partially labelled data is due to the high cost of labelled data. For this type of data, traditional rough set models cannot well represent the distribution of objects in real data. This limits effective decision-making and classification of the data. Replacing the missing labels with prediction labels can compensate for the absence of labels to some extent. Then, multiset-valued data with prediction labels is considered. This paper studies semi-supervised attribute reduction for partially labelled multiset-valued data via a prediction label strategy. First, the distance between information values in a multiset-valued decision information system (MSVDIS) is constructed, and the tolerance relation on the object set of an MSVDIS is introduced. Then, a partially labelled multiset-valued decision information system (p-MSVDIS) is defined. Next, a prediction label strategy (i.e., the existing labels in a p-MSVDIS remain the same, and the missing labels are replaced with the prediction label) is proposed. Incidentally, a new MSVDIS is obtained, and the object set is reclassified by the decision attribute. Moreover, the dependence and conditional entropy in the obtained MSVDIS are proposed, and some properties are discussed. Semi-supervised attribute reduction algorithms in a p-MSVDIS via dependence and conditional entropy are designed. Finally, experiments on real datasets show that the prediction label strategy outperforms the traditional rough set approach and that the designed algorithms have better classification and outlier detection performance than the existing algorithms.

Artificial intelligence-based group decision making to improve knowledge transfer: the case of distance learning in higher education

ABSTRACT In this paper, we propose a method based on multicriteria classification and a dominancebased rough set approach (DRSA) to support teachers in decision making. The objective is to use teachers’ knowledge and preferences to identify ‘atrisk students’, i.e. students who are likely to drop out, and ‘leader students’, i.e. students who are likely to help their peers, in distance learning. The proposed method is composed of two phases: phase I builds collective decision rules from teachers’ preferences, and phase II classifies students into two decision classes: ‘atrisk students’ and ‘leader students’. This method was designed, tested, and validated in higher education, with teachers who have acquired rich experience in teaching in online-synchronous mode since the Covid-19 pandemic.

Urban design strategies fostering creative workers' sense of identity in creative and cultural districts in East Asia: An integrated knowledge-driven approach

In recent years, many cultural districts have become increasingly unsustainable due to the neglect of physical environment quality. Hence, researchers have thoroughly investigated the close relationship between the physical environment quality of cultural districts and creative workers' sense of identity in creative and cultural districts (CCDs). However, few studies have clarified the complex relationship between physical environment factors and creative workers' sense of identity from a systematic perspective. Therefore, this study aims to establish a hybrid multiple-criteria decision-making (MCDM) model to identify the relationship between physical environment factors and creative workers' sense of identity using an integrated knowledge-driven approach that combines the rough set approach (RSA) and the decision-making trial and evaluation laboratory (DEMATEL) method. A total of 81 CCD cases in East Asia were selected to validate the proposed hybrid MCDM model. As result, through RSA, four core attributes that can help creative workers to form a sense of identity in CCDs were extracted, they are the quality of public art, the order and cleanliness of the building facade, the open spaces supporting social activities and the green beautification degree and characteristics. Then, 23 rules (If…Then…) were established. Additionally, through DEMATEL, the dominant influence relationships among these attributes were clarified based on expert knowledge. A “decision path map” driven by the “integrated knowledge” was developed, which can contribute to formulating and implementing strategies for systematically improving the physical environments of CCDs and consequently fostering creative workers' sense of identity. Specifically, our results show that project managers and designers pay attention to maintaining the authenticity of historical buildings in CCDs and focus on creating appropriate color environments and personalized private spaces based on the audience's aesthetic preferences and private habits to evoke a sense of local identity and belonging in creative workers.