Hybrid Rough Set Research Articles

Emergent Model Based On Hybrid Rough Sets Systems

The development of many knowledge discovery methods provided us with a good foundation for building hybrid systems based on rough set theory for knowledge discovery in a database. The need for more effective methods of generating and maintaining global nonfunctional properties suggests an approach analogous to those of natural processes in generating emergent properties. An emergent model allows the constraints of the task to be represented more naturally and permits only pertinent task-specific knowledge to emerge in the course of solving the problem. This paper describes some basics of emergent phenomena and its implementation in the hybrid system of rough sets combined with other methods. Further, demonstrations and guidelines are presented on how to exploit emergent intelligence and extend the problem-solving capabilities of these hybrid systems.

A Hybrid Rule-Based Rough Set Approach to Explore Corporate Governance: From Ranking to Improvement Planning

This research introduces a rule-based decision-making model to investigate corporate governance, which has garnered increasing attention within financial markets. However, the existing corporate governance model developed by the Security and Future Institute of Taiwan employs numerous indicators to assess listed stocks. The ultimate ranking hinges on the number of indicators a company meets, assuming independent relationships between these indicators, thereby failing to reveal contextual connections among them. This study proposes a hybrid rough set approach based on multiple rules induced from a decision table, aiming to overcome these constraints. Additionally, four sample companies from Taiwan undergo evaluation using this rule-based model, demonstrating consistent rankings with the official outcome. Moreover, the proposed approach offers a practical application for guiding improvement planning, providing a basis for determining improvement priorities. This research introduces a rule-based decision model comprising ten rules, revealing contextual relationships between indicators through if–then decision rules. This study, exemplified through a specific case, also provides insights into utilizing this model to strengthen corporate governance by identifying strategic improvement priorities.

A nonadditive rough set model for long-term clinical efficacy evaluation of chronic diseases in real-world settings

The pursuit of clinical effectiveness in real-world settings is at the core of clinical practice progression. In this study, we address a long-term clinical efficacy evaluation decision-making problem with temporal correlation hybrid attribute characteristics. To address this problem, we propose a novel approach that combines a temporal correlation feature rough set model with machine learning techniques and nonadditive measures. Our proposed approach involves several steps. First, over the framework of granular computing, we construct a temporal correlation hybrid information system, the gradient method is employed to characterize the temporal attributes and the similarity between objects is measured using cosine similarity. Second, based on the similarity of gradient and cosine, we construct a composite binary relation of temporal correlation hybrid information, enabling effective classification of this information. Third, we develop a rough set decision model based on the Choquet integral, which describes temporal correlation decision process. We provide the ranking results of decision schemes with temporal correlation features. To demonstrate the practical applications of our approach, we conduct empirical research using an unlabeled dataset consisting of 3094 patients with chronic renal failure (CRF) and 80,139 EHRs from various clinical encounters. These findings offer valuable support for clinical decision-making. Two main innovations are obtained from this study. First, it establishes general theoretical principles and decision-making methods for temporal correlation and hybrid rough sets. Second, it integrates data-driven clinical decision paradigms with traditional medical research paradigms, laying the groundwork for exploring the feasibility of data-driven clinical decision-making in the field.

A hybrid rough set shuffled frog leaping knowledge inference system for diagnosis of lung cancer disease

Abundant medical data are generated in the digital world every second. However, gathering helpful information from these data is difficult. Gathering useful information from the dataset is very advantageous and demanding. Besides, such data also contain many extraneous features that do not influence the foreboding accuracy while diagnosing a disease. The data must eliminate these extraneous features to get a better diagnosis. Ultimately, the minimized information system will lead to a better diagnosis. In this paper, we have introduced an incremental rough set shuffled frog leaping algorithm for knowledge inference. The proposed algorithm helps find minimum features from an information system while handling complex databases with uncertainty and incompleteness. The proposed rough set shuffled frog leaping knowledge inference model works in two phases. In the initial phase, the incremental rough set shuffled frog leaping algorithm is used to get the most relevant features. Identifying the relevant features is carried out using a fitness function, which uses the rough degree of dependency. The use of the fitness function identifies the much information with the minimum number of features. The purpose of feature selection is to identify a feature subset from an original set of features without reducing the predictive accuracy and to scale back the computation overhead in the data processing. In the second phase, a rough set is utilized for knowledge discovery in perception with rule generation. The selection of decision rules is carried out based on the accuracy of the decision rule and a predefined threshold value. An empirical analysis of the lung disease information system and a comparative study is conducted. Experimental outcomes exhibit that hybrid techniques express the feasibility of the proposed model while achieving better classification accuracy.

Hybrid Rough Set With Black Hole Optimization-Based Feature Selection Algorithm for Protein Structure Prediction

In this paper, a new approach for hybridizing Rough Set Quick Reduct and Relative Reduct approaches with Black Hole optimization algorithm is proposed. This algorithm is inspired of black holes. A black hole is a region of spacetime where the gravitational field is so strong that nothing— not even light— that enters this region can ever escape from it. Every black hole has a mass and charge. In this Algorithm, each solution of problem is considered as a black hole and gravity force is used for global search and the electrical force is used for local search. The proposed algorithm is compared with leading algorithms such as, Rough Set Quick Reduct, Rough Set Relative Reduct, Rough Set particle swarm optimization based Quick Reduct, Rough Set based PSO Relative Reduct, Rough Set Harmony Search based Quick Reduct, and Rough Set Harmony Search based Relative Reduct.

Cognitive Properties of MADM and Hybrid Rough Sets for Efficient Healthcare Test Diagnosis

Healthcare test diagnostic comprises the methods of finding test results in accordance with the prescribed symptoms of disease. The logic used in terms of attribute values are not sufficient to quantify the actual conditions of diagnostics, tests, or prognostics. In other words, a patient’s diagnosis-test measurement is assumed to be very severe, severe, or starting to be severe. Torationalization the quantified value of severity is not always discrete. To solve this problem, we use multi attribute decision making methods to identify alternative attributes through a hybrid methodology of rough and fuzzy relations in apessimistic and optimistic parameter of covering RST in the domain of healthcare test diagnostic domain.

Corporate sustainability assessment based on rough-grey set theory

PurposeThe purpose of this paper is to design an inference engine to measure the level of readiness of each bank before starting the corporate sustainability auditing process. Based on the output of the designed inference engine, the audition team can decide about the audition resources and the auditing process.Design/methodology/approachIn this paper, the hybrid rough and grey set theory are used to design and create a rule model system to measure the sustainability level of banks. First, 16 rule models are extracted using rough set theory (RST), and the cross-validation of each model is done. Then, the grey clustering is used to combine the same condition attributes and improve the validity of the final model. A total of 16 new rule models are extracted based on the decreased condition attributes, and the best model is selected based on the cross-validation results.FindingsBy comparing the accuracy of rough-gray’s rule models and as a result of decreasing the condition attributes, a proper increase in the accuracy of all models is obtained. Finally, the Naive/Genetic/object-related reducts model with 95.6% accuracy is selected as an inference engine to measure new banks’ readiness level.Originality/valueSustainability measurement of banks based on RST is a new approach in the field of corporate sustainability. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration

The European Union (EU) energy policy for sustainable development has been the topic of continuous debate, research, and analysis, which frequently focused on objectives and the evaluation of quantitative and qualitative performance. Different approaches can be used for the assessment of sustainable development goals. The authors of the article conducted a literature review of relevant research papers dated 2016–2020. The most common are quantitative methods based on hard data. Some qualitative studies based on soft data are also available but rare. This article proposes hybrid Rough Set Data Envelopment Analysis (DEA) and Rough Set Network DEA models that integrate both approaches. Also, the models allow the inclusion of uncertainty in the underlying data. The article uses hard data of the International Energy Agency (IEA) and the results of the EU survey regarding the influence of the socio-economic environment on CO2 emissions in EU countries. The authors demonstrate that multifaceted and objective assessment is possible by merging concepts from the set theory and operational research.

Feature Selection and Performance Improvement of Malware Detection System using Cuckoo Search Optimization and Rough Sets

The proliferation of malware is a severe threat to host and network-based systems. Design and evaluation of efficient malware detection methods is the need of the hour. Windows Portable Executable (PE) files are a primary source of windows based malware. Static malware detection involves an analysis of several PE header file features and can be done with the help of machine learning tools. In the design of efficient machine learning models for malware detection, feature reduction plays a crucial role. Rough set dependency degree is a proven tool for feature reduction. However, quick reduct using rough sets is an NP-hard problem. This paper proposes a hybrid Rough Set Feature Selection using Cuckoo Search Optimization, RSFSCSO, in finding the best collection of reduced features for malware detection. Random forest classifier is used to evaluate the proposed algorithm; the analysis of results proves that the proposed method is highly efficient.

Air passengers forecasting for Australian airline based on hybrid rough set approach

Abstract Accurate and reliable air passenger demand is very important for policy-making and planning by tourism management as well as by airline authorities. Therefore, this article proposed a novel hybrid method based on rough set theory (RST) to construct decision rules for long-term forecasting of air passengers. Level (mean) and trend components are first estimated from the air passengers time series data using DES model in the formulation of the proposed hybrid method. Then the rough set theory is employed to combine the output of DES model and generated decision rules is used to forecasting air passengers. We compare the proposed approach with other time series models using a corrected classified accuracy (CCA) criterion. For the empirical analysis, yearly air transport passenger from 1992 to 2004 is used. Empirical results show that the proposed method is highly accurate with the higher corrected classified accuracy. Also, forecasting accuracy of the proposed method is better than the other time series approaches.

Hybrid Rough Set with Black Hole Optimization Based Feature Selection Algorithm for Protein Structure Prediction

Hybrid Rough Set with Black Hole Optimization Based Feature Selection Algorithm for Protein Structure Prediction

Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models

Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models

A novel rough set based affinity propagation method for cholesterol prediction from ABC transporter

In biological science, all functions of membrane protein are being modulated by membrane cholesterol. Cholesterol plays a prime role in plasma membrane. The present research emphasized on design of a hybrid rough set based affinity propagation model for prediction of cholesterol sequence from Adenosine Triphosphate Binding Cassette transporters (ABC transporters). Each and every time trans-membrane helix sequences of ABC are evaluated and targeted with cholesterol to find out new valid motif signature. Cholesterol sequences are generated using the formula like Cholesterol Recognition Aminoacid Consensus (CRAC) for forward pattern and reverse of CRAC is CARC for backward pattern. The hypothesis of cholesterol binding motif modulates the signalling pathway of helical membrane protein. Many types of membrane proteins are reported till now, among them G-Protein Coupled Receptors (GPCR) and ABC are important as they are needed for drug discovery. ABC transporter super family is aimed in this work. In experimental study, we explored our hybrid method to find out motif consensus. Finally, from the results we report that our novel technique work efficiently identifying valid motif consensus from ABC protein sequences and is helpful for clinical drug target for human diseases.

Water eutrophication assessment based on rough set and multidimensional cloud model

This investigation developed a hybrid rough set (RST) and multidimensional cloud model (RSMCM) to leverage the unique strengths of RST and cloud modeling to evaluate the trophic level. In the proposed hybrid model, RST is used to decrease the data scale and extract the qualitative rules, and the multidimensional cloud model is employed to quantitatively analyze the average values, uniformity and stability of water eutrophication. The experimental results reveal that the hybrid model achieves more accurate assessment results than other mainstream models. Therefore, the hybrid model is a promising alternative for a water eutrophication information system and offers a quantitative measure for evaluating the uniformity and stability of eutrophication in utilities management and for operations staff.

A hybrid approach of rough set and case-based reasoning to remanufacturing process planning

Remanufacturing, a process returning used products to at least as good as new condition, is increasingly recognized as an important part of the circular economy. Since returned used components for remanufacturing have varying conditions and different defects, remanufacturing is very time-consuming and labor-intensive. There is an urgent need to reuse knowledge generated from existing parts remanufacturing to rapidly create sound process planning for the new arrival of used parts. A hybrid method combing rough set (RS) and cased-based reasoning (CBR) for remanufacturing process planning is presented in this paper. RS is employed for features reduction and rapid determination of features’ weights automatically, and CBR is utilized to calculate the similarity of process cases to identify the most suitable solution effectively from case database. The application of the methodology is demonstrated in an example of remanufacturing process for a saddle guide. The results indicated that the quality of remanufactured products has been improved significantly. The method has been implemented in a prototype system using Visual Studio 2010 and Microsoft SQL Server2008. The results suggested that the hybrid RS–CBR system is feasible and effective for the rapid generation of sound process planning for remanufacturing.

A Hybrid Rough Set Theory-PSO Technique for Solving of Non-convex Economic Load Dispatch

This paper applies a novel hybrid rough set theory-particle swarm optimizer technique, namely rough particle swarm optimization (RPSO) algorithm, for solving nonconvex economic load dispatch (NELD) problem. The RPSO algorithm is based on the notion of rough patterns that uses rough values defined with upper and lower intervals in which represent a set of values. This RPSO method is suggested to deal with the practical constraints such as valve point loading effect, generation limitation, ramp rate limits and prohibited operating zones in the NELD problems. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with classical PSO and crazy PSO algorithms. The results of this study reveal that the proposed approach is able to find appreciable NELD solutions than those of previous algorithms.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body’s resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient’s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.

Attribute and Case Selection for NN Classifier through Rough Sets and Naturally Inspired Algorithms

Supervised classification is one of the most active research fields in the Artificial Intelligence community. Nearest Neighbor (NN) is one of the simplest and most consistently accurate approaches to supervised classification. The training set preprocessing is essential for obtaining high quality classification results. This paper introduces an attribute and case selection algorithm using a hybrid Rough Set Theory and naturally inspired approach to improve the NN performance. The proposed algorithm deals with mixed and incomplete, as well as imbalanced datasets. Its performance was tested over repository databases, showing high classification accuracy while keeping few cases and attributes.

Attribute and Case Selection for NN Classifier through Rough Sets and Naturally Inspired Algorithms

Supervised classification is one of the most active research fields in the Artificial Intelligence community. Nearest Neighbor (NN) is one of the simplest and most consistently accurate approaches to supervised classification. The training set preprocessing is essential for obtaining high quality classification results. This paper introduces an attribute and case selection algorithm using a hybrid Rough Set Theory and naturally inspired approach to improve the NN performance. The proposed algorithm deals with mixed and incomplete, as well as imbalanced datasets. Its performance was tested over repository databases, showing high classification accuracy while keeping few cases and attributes.

Attribute and Case Selection for NN Classifier through Rough Sets and Naturally Inspired Algorithms

Supervised classification is one of the most active research fields in the Artificial Intelligence community. Nearest Neighbor (NN) is one of the simplest and most consistently accurate approaches to supervised classification. The training set preprocessing is essential for obtaining high quality classification results. This paper introduces an attribute and case selection algorithm using a hybrid Rough Set Theory and naturally inspired approach to improve the NN performance. The proposed algorithm deals with mixed and incomplete, as well as imbalanced datasets. Its performance was tested over repository databases, showing high classification accuracy while keeping few cases and attributes.