Variable Precision Rough Set Theory Research Articles

Shared manufacturing service evaluation based on intuitionistic fuzzy VIKOR

With the rise of the concept of sharing economy, the shared manufacturing model has gained widespread attention. The VIKOR shared manufacturing service evaluation approach is presented based on an intuitionistic fuzzy environment, which enables users to filter out acceptable shared manufacturing services from a wide pool of shared manufacturing services with similar functional qualities. Firstly, considering the QOS multi-indicator comprehensive evaluation of services by multiple stakeholders under the fundamental characteristics of shared manufacturing, the QOS evaluation index system is built from the two aspects of online and offline, which includes 2 first-level indicators and 10 second-level indicators. The paper also constructs a service recommendation model considering supply and demand constraints. Secondly, the intuitionistic fuzzy numbers are introduced to define the non-quantitative indexes, and the G1-method and variable-precision rough set theory are used for the assignment, and the maximum entropy theory is utilized to integrate the assignment method to obtain the combination weights. Thirdly, the VIKOR method based on intuitionistic fuzzy sets is used to evaluate and rank the shared manufacturing candidate services, in which the combined benefits and minimum regret values of the groups are solved based on the intuitionistic fuzzy number similarity. Finally, the reliability and feasibility of the algorithm are verified with a real case.

A Discretization Algorithm Based on Forest Optimization Network and Variable Precision Rough Set

Discretization of multidimensional attributes can improve the training speed and accuracy of machine learning algorithm. At present, the discretization algorithms perform at a lower level, and most of them are single attribute discretization algorithm, ignoring the potential association between attributes. Based on this, we proposed a discretization algorithm based on forest optimization and rough set (FORDA) in this paper. To solve the problem of discretization of multi-dimensional attributes, the algorithm designs the appropriate value function according to the variable precision rough set theory, and then constructs the forest optimization network and iteratively searches for the optimal subset of breakpoints. The experimental results on the UCI datasets show that:compared with the current mainstream discretization algorithms, the algorithm can avoid local optimization, significantly improve the classification accuracy of the SVM classifier, and its discretization performance is better, which verifies the effectiveness of the algorithm.

Failure Mode and Effects Analysis Using Variable Precision Rough Set Theory and TODIM Method

Failure mode and effects analysis (FMEA) is a widely used tool of risk assessment to identify and eliminate failures of products and systems. However, the conventional FMEA has some defects, such as the same importance of risk factors, utilizing crisp numbers to evaluate failures without considering vagueness. Although fuzzy methods are used to improve the conventional FMEA, the fuzzy FMEA requires much priori information (e.g., fuzzy membership function), which cannot flexibly reflect the changes of a decision maker's preference. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. Actually, FMEA members’ judgments on the failure modes are often influenced by their bounded rationality. To solve those problems, an integrated FMEA model is proposed in this paper, which integrates the strength of variable precision rough set theory in handling vagueness and the merit of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) approach in manipulating bounded rationality of decision makers. Finally, the proposed method is validated with a case study of a steam valve system to demonstrate its effectiveness and efficiency.

Random Forest Classifier based on Variable Precision Rough Set Theory

Random Forest Classifier based on Variable Precision Rough Set Theory

A multi-attribute decision-making model for the robust classification of multiple inputs and outputs datasets with uncertainty

Many multiple-criteria decision-making (MCDM) methods have been proposed for decision-making environments. However, the performance of these methods is degraded by the uncertainty and inaccuracy which characterizes most practical decision-making environments as a result of the inherent prejudices and preferences of the decision-makers or experts and an insufficient volume of multiple inputs and outputs (MIO) information. Accordingly, the present study proposes an enhanced MIO classification method to address these limitations of existing MCDM methods. The proposed MIO classification method designated as the FVM-index method integrates fuzzy set theory (FST), variable precision rough set (VPRS) theory, and a modified cluster validity index (MCVI) function, and is designed specifically to filter out the uncertainty and inaccuracy inherent in the surveyed MIO real-valued dataset; thereby improving the classification performance. The effectiveness of the proposed approach is first demonstrated by comparing the MIO classification results obtained for three relating UCI datasets: (1) the original dataset; (2) a dataset with a large amount of inaccurate instances; and (3) an FVM-index filtered dataset extracted from the original dataset using a statistical approach. Then, the validity of the proposed approach is illustrated by using an Augmented Reality product design and a hospital related datasets. The results confirm that the proposed FVM-index method provides a good classification performance even in the presence of inaccuracy and uncertainty. As a result, it provides a robust approach for the extraction of reliable decision-making rules.

Grey dominance variable precision rough set model based on grey dominance relationship

Purpose – The purpose of this paper is to construct a novel grey dominance variable precision rough model. Design/methodology/approach – To deal with the problems that the attribute values of the decision-making object are often not exact numbers but interval grey numbers, and the decision-making attributes satisfy a certain preference relationship in the decision-making information because of the complexity and uncertainty of the real world, the authors take advantage of the theoretical thinking of the grey systems, dominance rough set theory and variable precision rough set theory, and construct a novel dominance variable precision rough set model. On the basis of the thinking logic of grey systems, the authors first define the concepts of balance degree, dominance degree and inferior degree, and then the grey dominance relationship based on the comparison of interval grey numbers. Then the authors use the grey dominance relationship to substitute for the indiscernibility relationship of the variable precision rough set so that the grey dominance variable precision rough model is naturally utilized to reduce the system's attributes in order to derive the needed decision rules. At the end, the authors use a decision-making example of the radar target selection to demonstrate the feasibility and effectiveness of the novel model. Findings – The results show that the proposed model possesses certain fault tolerance ability and can well-realize decision rule extraction and knowledge discovery out of a given incomplete information system. Practical implications – The method exposed in the paper can be used to deal with the decision-making problems with the grey information, preference information and noise data. Originality/value – The paper succeeds in realizing both the grey decision-making information with preference information and noise data and the extraction of decision-making rules.

Application of Variable Precision Rough Set in Power Transformer Fault Diagnosis

The variable precision rough set theory is introduced into the fault diagnosis of power transformer. Using the reduction method of the variable precision rough set,the hidden information in power transformer faults data is reduced , and the information which plays a major role in fault classification can be obtained. This approach can overcome the defects of the classical rough set, such as the sensitivity to noise of input information, and accordingly improves the accuracy of fault diagnosis. The example shows that the variable precision rough set used in the power transformer fault diagnosis, enhance the robustness of the data analysis and processing, so, the proposed approach has a more effective diagnostic performance.

Application of Variable Precision Rough Set and Integrated Neural Network to Bearing Fault Diagnosis

The integration of variable precision rough set and neural network is introduced into the bearing fault diagnosis. VPRS-INN fault diagnosis method is proposed: First, utilize the information entropy method for discretization of continuous attributes, and then use attribute dependence degree of the variable precision rough set theory for heuristic reduction. based on the reduction, obtain the optimal decision support system. Finally according to the optimal design system, we design a integrated neural network for fault diagnosis. instances have proved the feasibility and high fault diagnosis rate of the method.

Generalized probabilistic approximations of incomplete data

In this paper we discuss a generalization of the idea of probabilistic approximations. Probabilistic (or parameterized) approximations, studied mostly in variable precision rough set theory, were originally defined using equivalence relations. Recently, probabilistic approximations were defined for arbitrary binary relations. Such approximations have an immediate application to data mining from incomplete data because incomplete data sets are characterized by a characteristic relation which is reflexive but not necessarily symmetric or transitive. In contrast, complete data sets are described by indiscernibility which is an equivalence relation.The main objective of this paper was to compare experimentally, for the first time, two generalizations of probabilistic approximations: global and local. Additionally, we explored the problem how many distinct probabilistic approximations may be defined for a given data set.

Variable precision intuitionistic fuzzy rough sets model and its application

Rough set theory is an important mathematical tool to deal with insufficient and incomplete information. The concepts of intuitionistic fuzzy rough sets and variable precision rough sets are very useful in the study of intelligent systems. The aim of this paper is to present a new extension of the rough set theory by means of integrating the variable precision rough set theory with the intuitionistic fuzzy rough set theory, i.e., the variable precision intuitionistic fuzzy rough set model is presented based on the intuitionistic fuzzy inclusion sets and intuitionistic fuzzy inclusion ratio which are defined in this paper, and by employing the intuitionistic fuzzy implicator and the intuitionistic fuzzy t-norm. Meanwhile, the approximation quality and attribute reduction of the variable precision intuitionistic fuzzy rough sets are defined. It shows that the results obtained in this paper extend the previous related conclusions. Finally, an example is given to illustrate our results.

Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression

Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significat differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

A heuristic approach to classifying labeled/unlabeled data sets

A classification method, which comprises Fuzzy C-Means method, a modified form of the Huang-index function and Variable Precision Rough Set (VPRS) theory, is proposed for classifying labeled/unlabeled data sets in this study. This proposed method, designated as the MVPRS-index method, is used to partition the values of per conditional attribute within the data set and to achieve both the optimal number of clusters and the optimal accuracy of VPRS classification. The validity of the proposed approach is confirmed by comparing the classification results obtained from the MVPRS-index method for UCI data sets and a typical stock market data set with those obtained from the supervised neural networks classification method. Overall, the results show that the MVPRS-index method could be applied to data sets not only with labeled information but also with unlabeled information, and therefore provides a more reliable basis for the extraction of decision-making rules of labeled/unlabeled datasets.

Using variable precision rough set for selection and classification of biological knowledge integrated in DNA gene expression.

DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provided biological knowledge has been important for classification. This paper presents a novel model for microarray data classification which is able to incorporate prior biological knowledge in the form of gene sets. Based on this knowledge, we transform the input microarray data into supergenes, and then we apply rough set theory to select the most promising supergenes and to derive a set of easy interpretable classification rules. The proposed model is evaluated over three breast cancer microarrays datasets obtaining successful results compared to classical classification techniques. The experimental results shows that there are not significant differences between our model and classical techniques but it is able to provide a biological-interpretable explanation of how it classifies new samples.

Optimized classification approach to modeling an expert system for selecting stock portfolio

An expert system for automatically selecting stock portfolio is presented. The expert system involved Grey Relational Analysis (GRA) model, the VPRS-index clustering / classification method, and Variable Precision Rough Set (VPRS) theory. The GRA model is applied to consolidate the 53 financial indices into six financial ratios (Grey Relational Grades (GRGs)) for each stock item. The VPRS-index method is used to determine the optimal number of clusters per GRG. VPRS theory is then applied to identify the stocks within the -lower approximate sets. Finally, the GRGs of each candidate stock item are consolidated to a single GRG indicating the ability of the stock item to maximize the rate of return. The validity and effectiveness of the VPRS-index clustering / classification method is first evaluated prior to that of the expert system. After that, results of this study showed that this expert system yields a higher rate of return than those of several existing portfolio selection systems. Key words: Fuzzy C-Means, VPRS, VPRS-index method, classification, stock portfolio.

A New Method of Neurofuzzy Network Based on Variable Precision Rough

In view of the current application deficiency of neuro-fuzzy network, a new optimal method of neurofuzzy network based on variable precision rough set is presented and its application in complex systems modeling is discussed. This method takes the β classification accuracy of variable precision rough set theory as information function to select the condition attribute, and then modeling data are discredited through selecting a proper precision to forms a decision table. Finally, the significant attributes and the key attribute values are extract from the decision table by using reduction algorithm based on variable precision, and are map pad into the fuzzy rules.It simplifies the fuzzy rules and therefore optimize the structure of neuro- fuzzy network effectively, reducing the training time of neural network greatly and improving the precision of training. This method has been applied to the modeling of non-linear time-delay system with a large number of sam-pling data, the validity and feasibility of this method is demonstrated by an example of modeling.

A Method of Gene-Function Annotation Based on Variable Precision Rough Sets

It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.

Modelling Satisfaction with Automated Banking Channels

The aim of this paper is to apply a new technique, i.e., Variable Precision Rough Set Theory (VPRS) to explaining consumer satisfaction with automated banking channels. VPRS is a new development on the original Rough Set Theory (RST) (for a discussion of RST see Slowinski et al. (1997) or Dimitras et al. (1999), for a discussion of VPRS see Ziarko, 1993a or 1993b), that not only estimates deterministic rules like RST (If … then …) but also probabilistic rules as well. The major advantage of these rules is that marketing managers can easily understand them, and this allows these managers to focus limited resources on meeting or exceeding the needs of particular consumers. A fairly simple model is used in this paper to illustrate the use of VPRS, which links overall satisfaction from ATM's to nine exogenous variables.This paper is divided into five parts, the first of which is a brief introduction to the technique of Rough Set Theory. The second describes developments in RST, VPRS and the literature on customer satisfaction. The third section describes the methodology and data collection. The fourth section analyses the empirical results derived from applying the VPRS model. Finally the last section discusses the results and managerial implications of the results obtained from this study and its implications for marketing management. To date VPRS has only been applied to a very small number of business research environments (see Beynon et al. (2001) or Beynon and Peel (2002)).

An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown. Scope and purpose The Monopolies and Mergers Commission (MMC) in the UK evaluates whether a given firm, or set of firms is behaving in a manner that is considered to be against the public interest, that is anti-competitive. The interpretation and prediction of the decisions made by the MMC is of importance to firm's possible future investment plans. Through the construction of decision rules using the variable precision rough sets (VPRS) model this interoperation and prediction is able to be undertaken. The importance of the concomitant variables in the decisions made is shown through a ‘leave n out’ Monte Carlo simulation approach. At the technical level this study illustrates one of the first applications of VPRS in an economic environment.

Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.