Fuzzy Rough Model Research Articles

Developing an intuitionistic fuzzy rough new correlation coefficient approach for enhancing robotic vacuum cleaner.

An intuitionistic fuzzy rough model is a powerful tool for dealing with complex uncertainty and imprecision in graph-based models, combining the strengths of intuitionistic fuzzy sets and rough sets. In this research, a correlation coefficient is an established tool for finding the strength of the relationship between two intuitionistic fuzzy rough graphs since correlation coefficients are very capable of processing and interpreting data. Furthermore, an intuitionistic fuzzy rough environment is integrated with attribute decision-making based on correlation coefficients. In order to measure the correlation between two intuitionistic fuzzy rough graphs, this suggests utilising the correlation coefficient concept and weighted correlation coefficient. In order to identify decision-making issues that are supported by intuitionistic fuzzy rough preference relations, the Laplacian energy and new correlation coefficient of intuitionistic fuzzy rough graphs are calculated in this study. We propose a new approach to computing the relative position loads of establishments by adjusting the correlation coefficient between one personality's intuitionistic fuzzy rough preference relation and the other items, as well as the undecided corroboration of the intuitionistic fuzzy rough preference relation. This paper determines the ranking order of all alternatives and the best one by using the correlation coefficient between each option and the ideal choice. In the meantime, the appropriate example improves decision-making for robotic vacuum cleaners by effectively handling uncertain and imprecise data, thereby optimising cleaning performance.

An intuitionistic fuzzy rough model for maritime transportation chains under disaster effects: the case of COVID-19

The outbreak of COVID-19 has caused disruptions in port transport infrastructure and shipping, resulting in higher shipping rates in 2022. This article aims to assess the extent of COVID-19 risks on the capacity and concentration of traffic flows, and evaluate the degree of imbalance in the transport system during 2020-2021 compared to the previous period of 2018-2019. The data is analyzed using the Intuitionistic Fuzzy Rough set, which allows the evaluation of multiple heterogeneous measures to produce an aggregate outcome. The model is based on the intensity of non-stationary incoming and outgoing traffic, as well as the intervals between separate ship calls. The data obtained from the study shows the dynamics of changes in traffic flows under the influence of COVID-19. The results indicate a minimal degree of its influence, which did not lead to any failures or modifications in the composition of traffic flows. The method can also allow for the development of complex simulation models to assess the limit of potential deviations, after which, with a high degree of probability, deformation of traffic flows is possible.

Fuzzy rough unlearning model for feature selection

In big data era, some data, becoming meaningless or illegal over time and space, need to be deleted from historical knowledge. It is a challenging problem, called machine unlearning, to efficiently forget the information of those outdated data from historical models. Some unlearning techniques have been proposed in loss-well-defined classification models, such as SVM, Random Forest, and Federated learning model. Yet, it is under study to remove outdated data from learned feature selection in fuzzy rough philosophy. To narrow this gap, we propose a fuzzy rough unlearning model for feature selection. Specifically, the outdated information is first identified in a compact set, called unlearning set, that remarkably shrinks the search space of feature selection. Then, the unlearning mechanisms containing two main theorems are proposed by leveraging unlearning set, followed by a feature selection unlearning algorithm. Theoretical analyses verify that the proposed unlearning algorithm is equivalent to the traditional algorithm retraining on the remaining data with the beginning of historical results. Experimentally, extensive results on 20 datasets demonstrate that our proposed unlearning methods perform effectively with remarkably less time cost. For the source code of ARU, please refer to: https://github.com/yuxin370/ARU.

Generalized Interval Type-II Fuzzy Rough Model-Based Feature Discretization for Mixed Pixels

Feature discretization algorithms of remote sensing images are often based on the assumption that a sample only belongs to a single category and cannot describe uncertainty caused by mixed pixels. Fuzzy rough models quantify uncertain information by introducing the memberships of pixels to each category. However, there are large errors in the decomposition model of mixed pixels, making the obtained memberships fuzzy. To overcome this weakness, we propose a feature discretization algorithm based on the generalized interval type-II fuzzy rough set for mixed pixels (GIT2FRSD). We use the fuzzy mean vector and the fuzzy covariance matrix to calculate the primary grades of pixels to each ground object and determine the secondary grades according to the distribution of pixels in the boundary region of the rough set. Then, we construct the fitness function using the magnitude of the reduction of the number of breakpoints and the average approximation precision of the generalized interval type-II fuzzy rough set and search for the best discrete breakpoints in all bands of the remote sensing image using an adaptive genetic algorithm. Our method further fuzzifies the abundance information, more accurately quantifying and evaluating the uncertainty caused by mixed pixels at a time complexity similar to that of the fuzzy rough model. The experimental results on GF-2 and Landsat 8 images show that compared with current mainstream discretization algorithms, our method has better search efficiency. It obtains the minimum number of discrete intervals while ensuring data consistency and achieves the highest classification accuracy.

Confidence Level Aggregation Operators Based on Intuitionistic Fuzzy Rough Sets With Application in Medical Diagnosis

In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in the way of medical diagnosis. In this paper, we try to develop some novel techniques that will help experts to diagnose diseases accurately. Based on a more advanced structure of intuitionistic fuzzy rough sets, in this article, we establish confidence-level intuitionistic fuzzy average/geometric aggregation operators to incorporate the familiarity degree of experts with evaluated objects for an initial assessment while intuitionistic fuzzy rough average/geometric aggregation operators cannot do so. Moreover, we have given some basic properties of the initiated operators. To show the effective use of these operators we have proposed an algorithm with an illustrative example. Furthermore, based on the intuitionistic fuzzy rough model, we have also established a medical diagnosis model to incorporate the difficulty that occurs in the diagnosis of disease. Furthermore, a comparative analysis demonstrates the efficiency of our proposed methods.

Exploiting fuzzy rough mutual information for feature selection

Feature selection is one of the important applications of rough set theory. Rough entropy proposed in rough set theory has been applied to feature selection. However, rough entropy is based on binary equivalence relation to divide object sets. Therefore, it applies only to nominal attribute data. To this end, this paper extends rough entropy to fuzzy rough set theory, and then proposes the fuzzy rough entropy in fuzzy approximate space. Fuzzy rough entropy decreases monotonically with the increase of the number of features. On this basis, the concepts of fuzzy joint rough entropy, fuzzy conditional rough entropy and fuzzy rough mutual information are defined. In order to measure the importance of features, inner and outer significance functions are constructed by making use of fuzzy rough mutual information. Furthermore, based on the proposed fuzzy rough entropy model, the corresponding feature selection algorithm is designed. It can directly deal with not only nominal data, but also numerical data and even mixed data. The use of inner and outer heuristic significance functions makes the proposed method select features from two complementary perspectives, so that the reduced feature set has better classification performance. In this method, the redundant features are effectively deleted by the backward redundancy elimination strategy. The proposed algorithm is compared with other algorithms on public data. The experimental results show that the proposed method is adaptive and effective.

Federated Neural Architecture Search for Medical Data Security

Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people’s panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.

Weighted fuzzy rough sets-based tri-training and its application to medical diagnosis

The theory of fuzzy rough sets is an effective soft computing paradigm for dealing with vague, uncertain, or imprecise data. However, most existing fuzzy rough sets-based methods may suffer from robustness since all samples are considered equally and also these methods are designed to cater for supervised or unsupervised learning. In this paper, we propose a weighted fuzzy rough sets-based multi-view tri-training model for partially labeled data. Specifically, considering the negative effect of noise, we first use a technique of data editing to filter potentially possible noises, and then a gradient descent algorithm is employed to optimize the weight of each sample with the objective of maximizing high-order weighted fuzzy dependency, based on which a robust weighted fuzzy rough set model is developed for labeled data. Moreover, we introduce the robust weighted fuzzy rough sets into tri-training and propose multi-view-based robust tri-training for partially labeled data by exploring data representations in the original view, the transformed view of principal component analysis, and the granular view after discretization. Extensive experiments conducted on UCI benchmark and medical diagnosis data sets show that the proposed model achieves favorable results in both supervised and semi-supervised scenarios.

A Fuzzy Rough Model Using Genetic Algorithm for Accuracy Enhancement

A Fuzzy Rough Model Using Genetic Algorithm for Accuracy Enhancement

A Feature Discretization Method Based on Fuzzy Rough Sets for High-Resolution Remote Sensing Big Data Under Linear Spectral Model

As one of the most relevant data preprocessing techniques, discretization has played an important role in data mining, which is widely applied in industrial control. It can transform continuous features to discrete ones, thus improving the efficiency of data processing and adapting to learning algorithms that require discrete data as inputs. However, traditional discretization methods have shortcomings, such as highly complex programs, excessive numbers of intervals obtained, and significant loss of necessary information in the preprocessing of high-resolution remote sensing big data. Moreover, the large number of mixed pixels in the image is a primary reason for the uncertainty of remote sensing information systems, and current discretization methods are based on the assumption that one pixel only corresponds to the spectral information of a single object, without considering the influence of the uncertainty caused by a mixed spectrum, which causes the classification accuracy to drop after discretization. We propose a discretization method for high-resolution remote sensing big data. We determine the membership degree of each pixel in training samples through linear decomposition and establish the individual fitness function based on a fuzzy rough model. An adaptive genetic algorithm selects discrete breakpoints, and a MapReduce framework calculates the individual fitness of the population in parallel to obtain the optimal discretization scheme in the minimum time. Our method is compared to the best state-of-the-art discretization algorithms on the authentic remote sensing datasets. Experiments verified the effectiveness of the proposed method, which provides strong support for the subsequent processing of images.

Rough fuzzy model based feature discretization in intelligent data preprocess

Feature discretization is an important preprocessing technology for massive data in industrial control. It improves the efficiency of edge-cloud computing by transforming continuous features into discrete ones, so as to meet the requirements of high-quality cloud services. Compared with other discretization methods, the discretization based on rough set has achieved good results in many applications because it can make full use of the known knowledge base without any prior information. However, the equivalence class of rough set is an ordinary set, which is difficult to describe the fuzzy components in the data, and the accuracy is low in some complex data types in big data environment. Therefore, we propose a rough fuzzy model based discretization algorithm (RFMD). Firstly, we use fuzzy c-means clustering to get the membership of each sample to each category. Then, we fuzzify the equivalence class of rough set by the obtained membership, and establish the fitness function of genetic algorithm based on rough fuzzy model to select the optimal discrete breakpoints on the continuous features. Finally, we compare the proposed method with the discretization algorithm based on rough set, the discretization algorithm based on information entropy, and the discretization algorithm based on chi-square test on remote sensing datasets. The experimental results verify the effectiveness of our method.

Big data analysis and prediction method of multidimensional space-time features of energy internet based on fuzzy rough model

Nowadays, energy has become a hot issue of concern to the whole society. With the unbalanced distribution of resources in the world and more severe climate change, the constraints of resource conditions and environmental status on global energy development are becoming stronger and stronger. The rapid development of the Internet, as well as the proposal of the energy Internet, has a better application in the analysis of energy demand, which can effectively alleviate the contradiction between energy and environment. Aiming at the big data of energy Internet and based on the advantages of fuzzy rough model, this paper studies a method of big data analysis and prediction of multidimensional space-time characteristics of energy Internet based on fuzzy rough model. Firstly, according to the spatio-temporal characteristics of energy Internet data, extract the multidimensional spatio-temporal characteristics of energy internet. Secondly, rough set and fuzzy set are two commonly used mathematical tools, and the combination of the two fuzzy rough models can more fully mine data information. In view of the shortcomings of the commonly used fuzzy rough set reduction algorithm, a reduction algorithm based on conditional entropy is proposed. Finally, taking multidimensional space-time characteristics as input, combining the advantages of fuzzy rough model and neural network, a prediction model is established to analyze and forecast energy demand. The simulation experiments show that the design method is feasible and superior, and can achieve the prediction of energy demand well, so as to make more rational use of energy.

Kernelized fuzzy rough sets based online streaming feature selection for large-scale hierarchical classification

In recent years, many online streaming feature selection approaches focus on flat data, which means that all data are taken as a whole. However, in the era of big data, not only the feature space of data has unknown and evolutionary characteristics, but also the label space of data exists hierarchical structure. To address this problem, an online streaming feature selection framework for large-scale hierarchical classification task is proposed. The framework consists of three parts: (1) a new hierarchical data-oriented kernelized fuzzy rough model with sibling strategy is constructed, (2) the online important feature is selected based on feature correlation analysis, and (3) the online redundant feature is deleted based on feature redundancy. Finally, an empirical study using several hierarchical classification data sets manifests that the proposed method outperforms other state-of-the-art online streaming feature selection methods.

A Rough-Fuzzy Model for Early Breast Cancer Detection

A Rough-Fuzzy Model for Early Breast Cancer Detection

Fuzzy Rough Set Based Technique for User Specific Information Retrieval

Information retrieval is widely used due to extremely large volume of text and image data available on the web and consequently, efficient retrieval is required. Text information retrieval is a branch of information retrieval which deals with text documents. Another key factor is the concern for a retrieval engine, often referred to as user-specific information retrieval, which works according to a specific user. This article performs a preliminary investigation of the proposed fuzzy rough sets-based model for user-specific text information retrieval. The model improves on the computational time required to compute the approximations compared to classical fuzzy rough set model by using Wikipedia as the information source. The technique also improves on the accuracy of clustering obtained for user specified classes.

Categorizing relational facts from the web with fuzzy rough sets

Significant advances have been made in automatically constructing knowledge bases of relational facts derived from web corpora. These relational facts are linguistic in nature and are represented as ordered pairs of nouns (Winnipeg, Canada) belonging to a category (City_Country). One major problem is that these facts are abundant but mostly unlabeled. Hence, semi-supervised learning approaches have been successful in building knowledge bases where a small number of labeled examples are used as seed (training) instances and a large number of unlabeled instances are learnt in an iterative fashion. In this paper, we propose a novel fuzzy rough set-based semi-supervised learning algorithm (FRL) for categorizing relational facts derived from a given corpus. The proposed FRL algorithm is compared with a tolerance rough set-based learner (TPL) and the coupled pattern learner (CPL). The same ontology derived from a subset of corpus from never ending language learner system was used in all of the experiments. This paper has demonstrated that the proposed FRL outperforms both TPL and CPL in terms of precision. The paper also addresses the concept drift problem by using mutual exclusion constraints. The contributions of this paper are: (i) introduction of a formal fuzzy rough model for relations, (ii) a semi-supervised learning algorithm, (iii) experimental comparison with other machine learning algorithms: TPL and CPL, and (iv) a novel application of fuzzy rough sets.

Intelligent design optimization of age-hardenable Al alloys

Softening at elevated temperatures is known to be a serious problem in aluminium alloys which restricts its wide span of application. Existing database on age-hardenable aluminium alloys (2XXX, 6XXX and 7XXX) is utilized in consolidation to design Al alloy with superior performance measured in terms of the mechanical properties, viz. yield strength, ultimate tensile strength and %Elongation, at different temperature regimes. Data on all three alloy groups are merged to a single database for developing the data-driven rough-fuzzy model, to utilize the effect of all possible precipitations and achieve stability of microstructure at elevated temperatures, without significantly compromising the ductility. The database consists of fifteen compositional and processing variables as the input variables, and three mechanical properties as the outputs. Rough-fuzzy approach is employed for modelling the complex alloy system due to its capability to bring out the most significant variables (removing redundant ones), and to formulate if-then rules to explain the system by adding a fuzzy element to it. This makes the model transparent also. The problem with conflicting objectives of increasing strength as well as ductility, is solved through multi-objective optimization using genetic algorithm to design alloys. The alloy is designed from the Pareto solutions and when developed experimentally shows promising results for further exploration.

Entropy Based Feature Selection for Fuzzy Set-Valued Information Systems

In Set-valued Information Systems (SIS), several objects contain more than one value for some attributes. Tolerance relation used for handling SIS sometimes leads to loss of certain information. To surmount this problem, fuzzy rough model was introduced. However, in some cases, SIS may contain some real or continuous set-values. Therefore, the existing fuzzy rough model for handling Information system with fuzzy set-values needs some changes. In this paper, Fuzzy Set-valued Information System (FSIS) is proposed and fuzzy similarity relation for FSIS is defined. Yager's relative conditional entropy was studied to find the significance measure of a candidate attribute of FSIS. Later, using these significance values, three greedy forward algorithms are discussed for finding the reduct and relative reduct for the proposed FSIS. An experiment was conducted on a sample population of the real dataset and a comparison of classification accuracies of the proposed FSIS with the existing SIS and single-valued Fuzzy Information Systems was made, which demonstrated the effectiveness of proposed FSIS.

A fuzzy rough multi-objective multi-item inventory model with both stock-dependent demand and holding cost rate

In this paper, we developed a multi-objective multi-item inventory model with fuzzy rough coefficients. Here, we have considered both demand and holding cost which is a non-linear function of the instantaneous stock level. Chance-constrained fuzzy rough multi-objective model and a traditional solution procedure based on an interactive fuzzy satisfying method are discussed. By examining the various definitions and theoretical results of fuzzy rough variables, we have designed a Tr-Pos chance constrained technique to determine the optimal solutions of a fuzzy rough multi-objective inventory problem. Finally, a numerical example is provided to illustrate the present model, and a sensitivity analysis of the optimal solution with respect to the major parameters is carried out.

Dynamic Upper Approximation Fuzzy Information Rough Communication Model

According to practical situations that any potentially useful information cannot be lost and the communicated concept is a dynamic fuzzy set, using the two-direction S-rough fuzzy set with dynamic characteristics, a dynamic fuzzy rough communication model is constructed. The concept of dynamic fuzzy information communication quantity is defined by using the cardinality of a fuzzy set, and the condition of information fidelity in the process of information communication is obtained. Dynamic fuzzy rough communication is a new application of two-direction S-rough fuzzy set theory.