Application Of Rough Sets Research Articles

Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network

Abstract Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.

Application of multi-valued rough neutrosophic set and matrix in multi-criteria decision-making

Rough set concept is a methodology of information processing for relational databases. It is a unique uncertainty mathematics topic closely connected to fuzzy set theory. When the rough set is combined with neutrosophic set theory, an effective tool for working with indeterminacy arises. In this study, we defined a multi-valued rough neutrosophic set and a multi-valued rough neutrosophic matrix. Using separation measures, we introduced a new approach for a multi-valued neutrosophic with a rough structure. We consider the problem of determining the condition of dengue-affected patients in a specific hospital. Using this method, we create a multi-valued rough neutrosophic decision matrix that clearly displays the relationship between patient conditions and symptoms. We can determine which one has a serious condition by solving this problem and presenting it on the graph.

Matrix-based feature selection approach using conditional entropy for ordered data set with time-evolving features

With the successful application of rough sets in many fields, research results on the theory emerge one after another. As one of the core contents of rough set theory, feature selection aims to find the minimum attribute set that does not affect the overall classification ability. In real life, there are often some data whose features will change with variables such as time, which is called ordered data with changing features. However, for ordered data with changing features, the existing methods in the current field are not applicable, because when the features of the data change, these methods basically need to recalculate from scratch to get a new reduction result, which is very time-consuming and does not use the previous reduction result. The feature incremental attribute reduction algorithm can be applied to the previous reduction results, thus greatly saving time. Drawing inspiration from this, this paper studies incremental attribute reduction algorithms under the order data with changing features. This paper first gives the entropy of the dominant condition of the dominant relation matrix and the updating principle of the new dominant relation matrix and the dominant diagonal matrix when the feature changes are explored. Later, two incremental attribute reduction algorithms HAR-A and HAR-D are also proposed in this paper, which are respectively applied to add features and delete features in ordered data with changing features. The subsequent experiments were also carried out on 9 data sets of UCI, and the performance of the proposed algorithm was evaluated. It can be seen from the experimental results that the two incremental attribute reduction algorithms we proposed are very effective.

Optimal scale selection based on multi-scale single-valued neutrosophic decision-theoretic rough set with cost-sensitivity

The selection of optimal scale has always been the essential problem of multi-scale system. However, most of the current studies only consider the consistency of the system, and ignore the cost information. Therefore, based on the ranking methods of single-valued neutrosophic number, this paper constructs a multi-scale dominant single-valued neutrosophic system. Furthermore, the test cost is defined according to the relative distance preference degree, and the risk cost of Bayesian theory introduced by decision-theoretic rough set is used as the decision cost of accepting, delaying and rejecting decisions. Therefore, we establish a multi-scale dominant single-valued neutrosophic decision-theoretic rough set based on test and decision cost. In addition, we also propose a scale updating algorithm to find out all consistent scales. Afterwards, we further raise an optimal scale selection algorithm based on the minimum total cost criterion. Finally, the algorithm is verified and analyzed by UCI data sets. The algorithm and model proposed in this paper further expand the application of single-valued neutrosophic rough set in multi-scale system, and provide a reference for subsequent research in this field.

ROUGH RINGS, ROUGH SUBRINGS, AND ROUGH IDEALS

The basic concept in algebra that is set theory can be expanded into rough sets. Basic operations on the set such as intersections, unions, differences, and complements can still apply to rough sets. In addition, one of the applications of rough sets is the use of rough matrices in decision making. Furthermore, mathematical or informatics researchers who work on rough sets connect the concept of rough sets with algebraic structures (groups, rings, and modules) so that a concept called rough algebraic structures is obtained. Since the research related to rough sets is mostly carried out at the same time, different concepts have emerged related to rough sets and rough algebraic structures. In this paper, other definitions of rough ring and rough subring will be given along with related examples and theorems. Furthermore, it will also be defined the left ideal and the right ideal of the rough ring along with examples. Finally, we will discuss the theorem regarding rough ideals.

Data Mining menggunakan Metode Rough Set dalam Memprediksi Tingkat Penjualan Peralatan Komputer

Data mining is a job in the form of collecting and using data to get a rule, pattern, or relationship in large data. The output of this data mining can be used to facilitate future decision-making. The purpose of this study is to predict the level of sales of computer equipment to make it easier for sellers to meet consumer needs. The data processed in this study include several factors which will later be included in the Roughtset method. The method used is a Rough set. Factors include the name of goods, warranty, price, and level of sales. These factors will later be grouped in the Equivalence Class, where the same attribute values ​​will be grouped. Then proceed to the next stage, namely the Discernibility Matrix which contains a collection of condition attributes that have different condition values. After that, proceed to the Discernibility Matrix Modulo D stage where the columns in the matrix are filled with a collection of condition attributes that have different conditions and different decision values. The next stage is Reduction, which is how to get the condition attributes used to get output in the form of knowledge. The last stage is knowledge which is the result of the reduction obtained. Then the results of the rough set application will be entered into the Rosetta application. The results obtained using the rough set method on 10 samples of computer equipment sales data, obtained 17 new rules or knowledge that can be used as guidelines in decision making to identify the level of computer sales.

Parallel incremental efficient attribute reduction algorithm based on attribute tree

Attribute reduction is an important application of rough sets. Efficiently reducing massive dynamic data sets quickly has always been a major goal of researchers. Traditional incremental methods focus on reduction by updated approximations. However, these methods must evaluate all attributes and repeatedly calculate their importance. When these algorithms are applied to large datasets with high time complexity, reducing large decision systems becomes inefficient. We propose an incremental acceleration strategy based on attribute trees to solve this problem. The key step is to cluster all attributes into multiple trees for incremental attribute evaluation. Specifically, we first select the appropriate attribute tree for attribute evaluation according to the attribute tree correlation measure to reduce the time complexity. Next, the branch coefficient is added to the stop criterion, increasing with the branch depth and guiding a jump out of the loop after reaching the maximum threshold. This avoids redundant calculation and improves efficiency. Furthermore, we propose an algorithm for incremental attribute reduction based on attribute trees using these improvements. Finally, a Spark parallel mechanism is added to parallelize data processing to implement the parallel incremental efficient attribute reduction based on the attribute tree. Experimental results on the Shuttle dataset show that the time consumption of our algorithm is more than 40% lower than that of the classical IARC algorithm while maintaining its good classification performance. In addition, the time is shortened by more than 87% from the benchmark after adding the Spark parallelizing mechanism.

Multigranulation Rough Set Methods and Applications Based on Neighborhood Dominance Relation in Intuitionistic Fuzzy Datasets

Multigranulation Rough Set Methods and Applications Based on Neighborhood Dominance Relation in Intuitionistic Fuzzy Datasets

Packaging Design Method of Modern Cultural and Creative Products Based on Rough Set Theory

Nowadays, the popularity of cultural and creative industry in China is rising, and cultural and creative products are loved by young people with high cultural added value. Through the field investigation, it is found that the tie-dyeing products in the market are still facing the problems of insufficient market awareness, the same tie-dyeing products, serious homogenization, and the aging of inheritors. Therefore, after the questionnaire survey, based on the needs of the audience, we proposed a packaging design method for modern cultural and creative products based on a rough set. As one of the mathematical tools to deal with uncertain knowledge, a rough set theory can solve the uncertainty caused by knowledge granularity in cognitive diagnosis. Without prior knowledge, we can get the corresponding decision-making or classification rules of the specific problem and classify the research objects. This study aimed to apply a rough set theory to the packaging design of modern cultural and creative products and overcome some problems existing in the packaging design methods of cultural and creative products. Since the application of rough set in the packaging design of cultural and creative products is a new topic, whether it is effective in the packaging design of cultural and creative products, how effective it is, and what problems it can deal with in the whole procedure of the design are worth discussing.

Soft Rough Set based span for unsupervised keyword extraction

The present work proposes an application of Soft Rough Set and its span for unsupervised keyword extraction. In recent times Soft Rough Sets are being applied in various domains, though none of its applications are in the area of keyword extraction. On the other hand, the concept of Rough Set based span has been developed for improved efficiency in the domain of extractive text summarization. In this work we amalgamate these two techniques, called Soft Rough Set based Span (SRS), to provide an effective solution for keyword extraction from texts. The universe for Soft Rough Set is taken to be a collection of words from the input texts. SRS provides an ideal platform for identifying the set of keywords from the input text which cannot always be defined clearly and unambiguously. The proposed technique uses greedy algorithm for computing spanning sets. The experimental results suggest that extraction of keywords using the proposed scheme gives consistent results across different domains. Also, it has been found to be more efficient in comparison with several existing unsupervised techniques.

A comparative study of different granular structures induced from the information systems

Binary information table, multi-valued information table and set-valued information table are three kinds of information systems often encountered in information processing. For any information system, we can often induce different information granular structures, and then construct the corresponding rough set models. Generally speaking, for the same information system, three models of Pawlak rough set, covering rough set and multi-granulation rough set can be induced according to different rules. These three kinds of rough set models are effective tools for data mining and information processing. This paper studies the relationship among Pawlak rough set, covering rough set and multi-granularity rough set induced in binary information table, multi-valued information table and set-valued information table, and obtains many important conclusions. The research content of this paper effectively connects the theories, methods and applications of Pawlak rough set, covering rough set and multi-granularity rough set, which not only enriches the rough set theory, but also expands the application prospect of rough set.

Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System.

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.

Applications of Rough Sets in Plant Sciences and Disease Diagnosis

The aim of this work is to introduce an approach for analysis of information system concerning plant diseases. This approach is based on Rough Set Theory and relied on extracting the original data as well as attribute reduction for the final diagnosis of the model of building a tomato disease to provide practical diagnostic tool. We provided an example that shows how to deal with tomato disease.

Application of Rough Set and Neural Network in Water Energy Utilization

In water energy utilization, the damage of fault occurring in the power unit operational process to equipment directly affects the safety of the unit and efficiency of water power conversion and utilization, so fault diagnosis of water power unit equipment is especially important. This work combines a rough set and artificial neural network and uses it in fault diagnosis of hydraulic turbine conversion, puts forward rough set theory based on the tolerance relation and defines similarity relation between samples for the decision-making system whose attribute values are consecutive real numbers, and provides an attribute-reducing algorithm by making use of the condition that approximation classified quality will not change. The diagnostic rate of artificial neural networks based on a rough set is higher than that of the general three-layer back-propagation(BP) neural network, and the training time is also shortened. But, the network topology of an adaptive neural-fuzzy inference system is simpler than that of a neural network based on the rough set, the diagnostic accuracy is also higher, and the training time required under the same error condition is shorter. This algorithm processes consecutive failure data of the hydraulic turbine set, which has avoided data discretization, and this indicates that the algorithm is effective and reliable.

Application of Rough Sets to Predict the Breast Cancer Risk Association with Routine Blood Analyses

For women around the globe, breast cancer has been a significant cause of mortality. Around the same time, early diagnosis and high cancer prediction precision are critical to improving the quality of care and the recovery rate of the patient. Expert systems and machine learning techniques are gaining prominence in this area as a result of efficient classification and high diagnostic ability. This paper introduces a model of hybrid prediction (RS QA) based on a rough set theoryand a quasi-optimal (AQ) rule induction algorithm. To find a minimal set of attributes that completely define the results, a rough set tool is used. The selected characteristics were collected, ensuring the high standard of the classification. Then to produce the decision rules, we use the quasi-optimal (AQ) rule induction algorithm. These hybrid prediction models allow expert systems to be built based on the conceptual rules of the IF THEN sort. The suggested experiment is performed using the Coimbra Breast Cancer Dataset (BCCD) based on sets of measures that can be obtained in routine blood tests. Using classification precision, sensitivity, specificity, and receiver operating characteristics (ROC) curves, the efficiency of our suggested approach was assessed. Experimental results indicate the highest classification accuracy (91.7 percent), sensitivity (83.3 percent), and precision (94.3) obtained by the proposed (RS_QA) model.

N-soft rough sets and its applications

In this paper, we propose a new hybrid model called N-soft rough sets, which can be seen as a combination of rough sets and N-soft sets. Moreover, approximation operators and some useful properties with respect to N-soft rough approximation space are introduced. Furthermore, we propose decision making procedures for N-soft rough sets, the approximation sets are utilized to handle problems involving multi-criteria decision-making(MCDM), aiming at electing the optional objects and the possible optional objects based on their attribute set. The algorithm addresses some limitations of the extended rough sets models in dealing with inconsistent decision problems. Finally, an application of N-soft rough sets in multi-criteria decision making is illustrated with a real life example.

Applications of rough sets in big data analysis: An overview

Applications of rough sets in big data analysis: An overview

Applications of probabilistic hesitant fuzzy rough set in decision support system

Applications of probabilistic hesitant fuzzy rough set in decision support system

Application of Fuzzy and Rough Sets to Environmental Zonation for Concrete Durability: A Case Study of Shaanxi Province, China

The durability of concrete structures is influenced by various factors, and the durability damage mechanism is different when the structure is in different environmental conditions. This will have implications for improving the durability of concrete structures and extending its service life if the special environmental condition is taken into account in the durability design. Aimed at the environmental zonation for concrete durability, this paper investigated the durability factors influencing concrete structures in Shaanxi Province, China, including atmospheric temperature, precipitation, corrosive gas, and acid rain. The variations of the above-mentioned factors were analyzed and the indexes of environmental zonation were proposed. According to the zoning principle, the weights of zoning indexes calculated using fuzzy rough sets were used to divide Shaanxi Province into three first-level zones, namely the Freeze-Thaw Cycle Zone, the Neutralization–Freeze-Thaw Interaction Zone, and the Neutralization Zone. These three zones were then subdivided into nine second-level zones. The main mechanism of concrete deterioration and the environmental characteristics of all zones were then analyzed. The method proposed in this paper puts forward clear zoning indexes and quantifies them, which can improve the quality and accuracy of the zoning results. Moreover, the research achievements are helpful for engineers to reduce the impact of the environment on structure and the maintenance cost during the structural service life to a certain extent.

Double-quantitative decision rough set over two universes and application to African swine fever decision-making

ABSTRACT In order to actively respond to the severe situation of the current African swine fever (ASF) epidemic, this paper proposed a double-quantitative decision rough set method over two universes. First, a double-quantitative decision rough set model over two universes based on compatibility relation is defined. Furthermore, the characteristics of ASF decision-making problems are fully considered, and the ASF decision-making process is transformed into a rough approximation decision problem. Then, we construct a double-quantitative decision rough set application method over two universes based on ASF decision-making problems. The upper and lower approximations of the ASF decision objects are calculated by the double quantification approximation space over two universes, and the positive region of the ASF decision object is given. The model proposes a method of ASF decision-making and the optimal decision rules based on the personal preference information of decision-makers. Finally, the theoretical model is applied to the ASF decision problem to illustrate the application process and its effectiveness of the model constructed in this paper. At the same time, the corresponding decision suggestions for dealing with the actual ASF problem are proposed based on the application results of numerical examples.