Probabilistic Variable Precision Fuzzy Rough Set Technique for Discovering Optimal Learning Patterns in E-learning

In order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.

The uncertainty of probabilistic rough sets in multi-granulation spaces

A comparative study of variable precision fuzzy rough sets based on residuated lattices

In this paper some properties of the variable precision fuzzy rough sets model will be considered. A new way of determining the positive region of classification will be proposed, which is useful in evaluation of approximation quality in variable precision fuzzy or crisp rough sets applications. The notions of the fuzzy rough weighted mean u-lower and l-upper approximation will be discussed. Fuzzy rough approximations will be evaluated basing on selected R-implicators.

Three-way decision making approach to conflict analysis and resolution using probabilistic rough set over two universes

The rough set theory (RST) can deal with the approximations of an arbitrary subset of a universe by two definable subsets called, respectively, the lower and the upper approximations. Probabilistic rough sets (PRS) model is an extension of RST. In this paper, we propose a new matrix view of PRS and give the matrix representation of the lower and upper approximations of PRS. According to this matrix view, a new method is obtained for calculation of the lower and upper approximations under PRS. An example is given to illustrate processes of calculating the approximations of a concept based on the matrix point of view.

Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified.

In the real world, we often encounter varying membership grades due to varying information source values. The fuzzy rough set model is refurbished to develop probabilistic variable precision fuzzy rough set (P-VP-FRS) to deal with this imprecision. The main inspiration behind the proposed P-VP-FRS is our inability to precisely represent the imprecision, which necessitates generalization in the approximations. The adjustable parameters in P-VP-FRS control the tradeoff between the generalization and accuracy. A few measures for quality of approximation and generalization are proposed. The usefulness of P-VP-FRS is shown through a case study.

This article introduces general framework of multi-granulation bipolar-valued fuzzy (BVF) probabilistic rough sets (MG-BVF-PRSs) models in multi-granulation BVF probabilistic approximation space over two universes. Four types of MG-BVF-PRSs are established, by the four different conditional probabilities of BVF event. For different constraints on parameters, we obtain four kinds of each type MG-BVF-PRSs over two universes. To find a suitable way of explaining and determining these parameters in each kind of each type MG-BVF-PRS, three-way decisions (3WDs) are studied based on Bayesian minimum-risk procedure, i.e., the multi-granulation BVF decision-theoretic rough set (MG-BVF-DTRS) approach. The main contribution of this paper is twofold. One is to extend the fuzzy probabilistic rough set (FPRS) to MG-BVF-PRS model over two universes. Another is to present an approach to select parameters in MG-BVF-PRS modeling by using the process of decision making under conditions of risk.

Probabilistic rough sets define the lower and upper approximations and the corresponding three regions by using a pair of (α,β) thresholds. Many attempts have been made to determine or calculate effective (α,β) threshold values. A common principle in these approaches is to combine and utilize some intelligent technique with a repetitive process in order to optimize different properties of rough set based classification. In this article, we investigate an approach based on genetic algorithms that repeatedly modifies the thresholds while reducing the overall uncertainty of the rough set regions. A demonstrative example suggests that the proposed approach determines useful threshold values within a few iterations. It is also argued that the proposed approach provide similar results to that of some existing approaches such as the game-theoretic rough sets.

Quantitative rough sets based on subsethood measures

Smart tourism destinations are characterized by a pervasive presence of new technologies able to influence and improve the quality of tourist experiences. Understanding how tourists perceive and evaluate the assets and point of interests of a smart tourism destination is the first step for supporting decision makers and urban planners in making coherent and informed decisions aiming at improving the competitiveness of the destination. In this paper, we present a novel approach based on Granular Computing and Rough Set Theory for the analysis of the collective perception of a community of users with respect to points of interest (POI) in a smart tourism destination. The approach supports the classification of the POIs with respect to the tourists' perception by leveraging on the Three-way decision model and Probabilistic Rough Sets. The creation of multi-level granular structures representing the users' opinions is an added value of the approach when dealing with many POIs and large communities of tourists. An illustrative example related to the city of Salerno is reported to demonstrate the capability of the approach in supporting decision makers in the evaluation of tourist experiences.

Generalized Fuzzy Neighborhood System-Based Multigranulation Variable Precision Fuzzy Rough Sets with Double Topsis Method to Madm

Application of a Novel Multi-granularity Variable Precision Fuzzy Rough Set in Attribute Reduction

This paper is devoted to propose generalized L-fuzzy variable precision rough sets, based on generalized residuated lattices with conjunctions not necessarily commutative over two universes, as a generalization of the notion of L-fuzzy variable precision rough sets based on residuated lattices with commutative conjunctions over one universe only. Then we define and investigate several classes of generalized L-fuzzy variable precision rough sets. The topological properties and granular representation of generalized L-fuzzy variable precision rough sets are also given. Our main concern is to propose a real number valued function for each approximation operator in generalized L-fuzzy variable precision rough sets and to measure its approximating ability. We observe that the functions of lower and upper approximation operators are natural generalizations of belief and plausibility functions respectively as given in the evidence theory. Using these functions, accuracy measure and degree of roughness is defined for generalized L-fuzzy variable precision rough sets. As an application of generalized L-fuzzy variable precision rough sets, I-fuzzy variable precision rough sets are proposed on the unit interval I = [0, 1], based on generalized residuated lattices induced by left-continuous pseudo-t-norms.