Fuzzy Neighbor Research Articles

AFNFS: Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data

The classification efficiency of majority classes for imbalanced data is so concerned in real-world applications. Almost fuzzy neighborhood radius still needs to be manually set and many entropy measures may ignore the boundary region of data, these limitations will result in the poor classification effect. To address these limitations, this paper designs a novel adaptive fuzzy neighborhood-based feature selection method for imbalanced data with adaptive synthetic over-sampling. First, the closeness is defined according to the variance distance between the samples of the minority class, the pair set of neighboring samples is designed, and then an improved adaptive synthetic over-sampling model is presented for constructing balanced decision systems consisting of the synthetic samples and original samples. Second, an adaptive fuzzy neighborhood radius is developed when using the data margins of all homogeneous and heterogeneous samples. Then the adaptive fuzzy neighborhood granule and upper and lower approximations are defined to construct a new FNRS model. Thus, approximate accuracy and roughness are presented to measure the uncertainty from the fuzzy and rough perspectives for imbalanced data. Third, by combining the roughness with adaptive fuzzy neighborhood entropy, adaptive fuzzy neighborhood joint entropy is constructed to evaluate the uncertainty in fuzzy neighborhood decision systems from two viewpoints of algebra and information. Then the reduced set and the significance of the feature are further developed. Finally, this improved adaptive synthetic over-sampling algorithm is designed to aim to build this balanced decision system, and an adaptive fuzzy neighborhood-based feature selection algorithm with the tolerance parameter is developed to achieve an optimal feature subset. Experiments on 26 imbalanced datasets demonstrate that the constructed algorithms compared to the other related algorithms are effective.

Multigranulation-Based Granularity Selection for Intuitionistic Fuzzy Weighted Neighborhood IoT Data

Granular computing is to represent, construct, and process information granules formalized in many different approaches, and different formal approaches emphasize the same fundamental facet in different ways. In this paper, we present the multigranulation rough set models in the intuitionistic fuzzy neighborhood information system generated by Internet of Things (IoT) data with weighted features and develop the basic properties of the proposed models. Moreover, the multigranulation-based optimal granularity selection approach is established by introducing the concept of granularity significance. The experimental results on eight IoT datasets demonstrate that the proposed methods exhibit better efficiency by comparing three classical methods under the intuitionistic fuzzy weighted neighborhood information system.

Three-way decision theory based on interval type-2 fuzzy linguistic term sets

This study examines decision theory based on interval type-2 fuzzy sets with linguistic information for the three-way decision approach by addressing the challenge of uncertainty for information analysis and fusion in subjective decision-making processes. First, the interval type-2 fuzzy linguistic term sets (IT2 FLTSs) are defined to represent and normalize the uncertain preference information in linguistic decision-making. Subsequently, perception computing based on computing with words paradigm is introduced to implement information fusion among different decision-makers in the linguistic information-based fuzzy logic reasoning process. Then, a three-way decision (3WD) theory based on IT2 FLTSs with fuzzy neighborhood covering is proposed, and the corresponded tri-partitioning strategies that satisfy Jaccard similarity of membership distributions are given. Finally, 3WD theory is applied to multi-criteria group decision-making with linguistic terms, and the algorithm steps are illustrated by a promising application under the background of coronavirus disease 2019 to reveal the feasibility and practicability of the proposed approach.

Label enhancement-based feature selection via fuzzy neighborhood discrimination index

As an essential preprocessing step, the feature selection for multi-label classification is an efficient tool to solve the high-dimensionality in training data under multiple semantics. However, in multi-label learning, the assumption of a uniform distribution is not practicable for some real applications where labels are related to the instance with different relative importance degrees. Motivated by this, the label enhancement algorithm is utilized to transform the logical values in multiple labels into label distributions with real values. To maintain the supervised information of label distributions, the discrimination index is combined with granular theory to describe the vagueness in feature and label spaces, and the fuzzy neighborhood discrimination index is presented to assess the discrimination information in features with continuous values. Furthermore, an innovative label enhancement-based feature selection approach is designed to handle the challenge caused by high dimensionality with multiple semantics. In this algorithm, the fuzzy conditional discrimination index is proposed by combining the fuzzy neighborhood discrimination index under the feature subset with the label similarity matrix of label distributions, and the significance of the feature is measured by the fuzzy conditional discrimination index, which is utilized to assess the discernibility information of labels under the feature. Furthermore, to validate the efficacy of our algorithm, a collection of experiments are conducted with five representative multi-label feature selection methods under twelve benchmark datasets. With six widespread evaluation indicators of multi-label classification, the experimental results indicate that our algorithm obtains superior performance against other algorithms.

Online group streaming feature selection using entropy-based uncertainty measures for fuzzy neighborhood rough sets

Online group streaming feature selection, as an essential online processing method, can deal with dynamic feature selection tasks by considering the original group structure information of the features. Due to the fuzziness and uncertainty of the feature stream, some existing methods are unstable and yield low predictive accuracy. To address these issues, this paper presents a novel online group streaming feature selection method (FNE-OGSFS) using fuzzy neighborhood entropy-based uncertainty measures. First, a separability measure integrating the dependency degree with the coincidence degree is proposed and introduced into the fuzzy neighborhood rough sets model to define a new fuzzy neighborhood entropy. Second, inspired by both algebra and information views, some fuzzy neighborhood entropy-based uncertainty measures are investigated and some properties are derived. Furthermore, the optimal features in the group are selected to flow into the feature space according to the significance of features, and the features with interactions are left. Then, all selected features are re-evaluated by the Lasso model to discard the redundant features. Finally, an online group streaming feature selection algorithm is designed. Experimental results compared with eight representative methods on thirteen datasets show that FNE-OGSFS can achieve better comprehensive performance.

(L,M)-Fuzzy k-Pseudo Metric Space

Recently, the notion of a classical k-metric, which make the triangle inequality to a more general axiom: d(x,z)≤k(d(x,y)+d(y,z)), has been presented and is applied in many fields. In this paper, the definitions of an (L,M)-fuzzy k-pseudo metric and an (L,M)-fuzzy k-remote neighborhood ball system are introduced. It is proved that the category of (L,M)-fuzzy k-pseudo metric spaces is isomorphic to the category of (L,M)-fuzzy k-remote neighborhood ball spaces. Besides, (L,M)-fuzzy topological structures induced by an (L,M)-fuzzy k-pseudo metric are presented and their properties are investigated. Finally, the concept of a nest of pointwise k-pseudo metrics is proposed and it is shown that there is a one-to-one correspondence between (L,M)-fuzzy k-pseudo metrics and nests of pointwise k-pseudo metrics.

Fuzzy Graph Cellular Automaton and Its Applications in Parking Recommendations

The scope of this paper is to make the best use of cellular automaton. It is important that they can simulate not just a discrete model but also used to solve practical problems. To stimulate the research in this field, we define Fuzzy Graph Cellular Automaton (FGCA) and classify the fuzzy rule matrix according to the rules of the cellular automaton. We also provide the details of the generations of FGCA. To cover the defined concept, the parking recommendations have been figured out to show the effective performance of the research. In this proposed model, the fuzzy neighbourhood function represents the possible cell to which the vehicle can moved so that an efficient parking management can be maintained. By using fuzzy graph cellular automaton in parking recommendations, the results are more accurate than the other models. A comparative analysis is also done. In parking recommendations, the possibility of the available parking space can be predicted appropriately using the defined concepts. The results are simulated with [Formula: see text] coding in MATlab.

Comprehensive fuzzy concept-oriented three-way decision and its application

Currently, more and more researchers use the idea and strategy of three-way decision to guide scientific research. In this paper, we propose two comprehensive fuzzy concepts and apply them to construct a new three-way decision model. Firstly, with the TOPSIS method, this paper regards the optimal distance and the worst distance as the cost fuzzy concept and the benefit fuzzy concept, respectively. Then, based on the fuzzy concepts with opposite characteristics, two comprehensive fuzzy concepts (i.e., the comprehensive benefit fuzzy concept and the comprehensive cost fuzzy concept) are proposed, and the relationship between the decision-making regions corresponding to the two comprehensive fuzzy concepts is discussed. Further, four fuzzy neighborhood operators are used to describe the relationship between any two schemes and the relative loss function is used to compute the loss value of each scheme with different behaviors in different states. Afterwards, we introduce a new three-way decision model to solve the multi-criteria decision-making (MCDM) problem. Comparing analysis, Kendall analysis and Spearman analysis show that our method is feasible and stable.

Dynamic maintenance of variable precision fuzzy neighborhood three-way regions in interval-valued fuzzy decision system

Dynamic maintenance of variable precision fuzzy neighborhood three-way regions in interval-valued fuzzy decision system

Multi-label feature selection based on fuzzy neighborhood rough sets

Multi-label feature selection, a crucial preprocessing step for multi-label classification, has been widely applied to data mining, artificial intelligence and other fields. However, most of the existing multi-label feature selection methods for dealing with mixed data have the following problems: (1) These methods rarely consider the importance of features from multiple perspectives, which analyzes features not comprehensive enough. (2) These methods select feature subsets according to the positive region, while ignoring the uncertainty implied by the upper approximation. To address these problems, a multi-label feature selection method based on fuzzy neighborhood rough set is developed in this article. First, the fuzzy neighborhood approximation accuracy and fuzzy decision are defined in the fuzzy neighborhood rough set model, and a new multi-label fuzzy neighborhood conditional entropy is designed. Second, a mixed measure is proposed by combining the fuzzy neighborhood conditional entropy from information view with the approximate accuracy of fuzzy neighborhood from algebra view, to evaluate the importance of features from different views. Finally, a forward multi-label feature selection algorithm is proposed for removing redundant features and decrease the complexity of multi-label classification. The experimental results illustrate the validity and stability of the proposed algorithm in multi-label fuzzy neighborhood decision systems, when compared with related methods on ten multi-label datasets.

Hierarchical cognize framework for the multi-fault diagnosis of the interconnected system based on domain knowledge and data fusion

An interconnected system (ICS) is a complex industry system with multiple sensors, multiple tasks, and massive interaction. It is also of great importance for conducting the fault diagnosis technology research. Multi-fault diagnosis (MFD) is an urgent problem in engineering, while the complex mapping relationships among the system sensors, data patterns in single sensors, and fault modes in ICSs bringing severe challenges. The faults of ICS are similar to human disease in multiple dimensions. Enlightening the understanding of diseases in medicine guides us: hierarchical cognition and knowledge-data-fusion are important systematic ideas. Inspired by these, we propose a hierarchical cognize framework (HCF), which covers the cognition of sensors, data patterns in single sensors, and data climates. Subsequently, we propose a fuzzy neighbourhood three-way decision (FN3WD), experience fused self-adaptation Gaussian-mixture-model (EFSA-GMM), and coding-with-knowledge-discrimination (CWKD) to construct an HCF. To comprehensively verify the HCF, we successfully apply the HCF to the MFD of a satellite power system. Classic models of two-mainstream strategies are introduced as comparisons, specifically, MC-DCNN, MC-SVM, ML-DCNN, and ML-SVM. Compared to the comparative models, the HCF performs an increase of 12.35%, 7.72%, 6.90%, and 8.10% at least in accuracy, precision, recall, and F1-score, respectively, in 10 times cross-validation. Benefitting from the fusion of knowledge, the HCF has cognitive advantages in obtaining a high accuracy and precision diagnosis results. Meanwhile, the time consumption of the HCF is approximately 130 s, which is considerably reduced by as much as 50% compared with the deep learning models.

Feature selection method for color image steganalysis based on fuzzy neighborhood conditional entropy

Feature selection method for color image steganalysis based on fuzzy neighborhood conditional entropy

Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools

Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.

Some New Covering-Based Multigranulation Fuzzy Rough Sets and Corresponding Application in Multicriteria Decision Making

Multigranulation rough set theory is an important tool to deal with the problem of multicriteria information system. The notion of fuzzy β -neighborhood has been used to construct some covering-based multigranulation fuzzy rough set (CMFRS) models through multigranulation fuzzy measure. But the β -neighborhood has not been used in these models, which can be seen as the bridge of fuzzy covering-based rough sets and covering-based rough sets. In this paper, the new concept of multigranulation fuzzy neighborhood measure and some types of covering-based multigranulation fuzzy rough set (CMFRS) models based on it are proposed. They can be seen as the further combination of fuzzy sets: covering-based rough sets and multigranulation rough sets. Moreover, they are used to solve the problem of multicriteria decision making. Firstly, the definition of multigranulation fuzzy neighborhood measure is given based on the concept of β -neighborhood. Moreover, four types of CMFRS models are constructed, as well as their characteristics and relationships. Then, novel matrix representations of them are investigated, which can satisfy the need of knowledge discovery from large-scale covering information systems. The matrix representations can be more easily implemented than set representations by computers. Finally, we apply them to manage the problem of multicriteria group decision making (MCGDM) and compare them with other methods.

Redefined fuzzy rough set models in fuzzy β-covering group approximation spaces

The research purpose of this paper is to explore some new fuzzy rough set models in the fuzzy β-covering group approximation space (FβCGAS) and study the related properties, thereby provide new thinking directions for the theoretical development and application expansion of fuzzy rough set models. Firstly, in the fuzzy β-covering approximation space (FβCAS), in light of some existing fuzzy covering rough set (FCRS) models which do not satisfy the inclusion relations between the fuzzy upper and lower approximations, we define the concepts of parameterized fuzzy β-neighborhoods and parameterized fuzzy complementary β-neighborhoods to eliminate part of the noise data and establish four types of parameterized FCRS models that satisfy the inclusion relationship between the fuzzy lower and upper approximations. Meanwhile, the properties of the models are discussed. Further, in the FβCGAS, we define the concepts of the parameterized pessimistic (optimistic) fuzzy β-fitting neighborhoods and the parameterized pessimistic (optimistic) fuzzy complementary β-fitting neighborhoods and construct the parameterized pessimistic and optimistic FCRS models that satisfy the inclusion relationship between the fuzzy lower and upper approximations. At the same time, we likewise explore the properties of the models. Besides, we further use the newly proposed parameterized fuzzy neighborhoods to study the variable precision multi-granulation fuzzy decision-theoretic rough set models in the FβCGAS. Finally, for all the models mentioned above, we recommend them for solving the criteria weight determination problem, ranking problem, optimal selection problem and qualitative classification problem in multi-criteria decision-making. Meanwhile, we complete the relationships of all the new models mentioned in this paper and discuss them.

A novel three-way decision approach in decision information systems

Given that the three-way decision (3WD) theory provides a scientific solution and reasonable semantic interpretation for solving multi-criteria decision-making (MCDM) problems, this paper presents a new 3WD model and applies it to realistic MCDM problems. Since the fuzzy neighborhood operator is effective in handling uncertain data, we first define a pair of probabilistic approximation operators via the constructed α-fuzzy neigh borhood class. Then, a 3WD model is explored in decision information systems to address the related issues in the proposed fuzzy probabilistic rough set model. In light of the above works, we establish a novel approach to classify and rank applicants for enterprise talent recruitment problems. Instead of the rule of tie-breaking, a way is given to separate applicants into three parts by combining decision attributes, and the feasibility of the approach is confirmed as well. Finally, by using a data set in the UCI database, the results of comparative and experimental analyses demonstrate that the constructed MCDM approach owns better performances in terms of effectiveness and stability.

An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs

The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario.

A framework of decision making based on bipolar fuzzy competition hypergraphs

Bipolarity plays a key role in different domains such as technology, social networking and biological sciences for illustrating real-world phenomenon using bipolar fuzzy models. In this article, novel concepts of bipolar fuzzy competition hypergraphs are introduced and discuss the application of the proposed model. The main contribution is to illustrate different methods for the construction of bipolar fuzzy competition hypergraphs and their variants. Authors study various new concepts including bipolar fuzzy row hypergraphs, bipolar fuzzy column hypergraphs, bipolar fuzzy k-competition hypergraphs, bipolar fuzzy neighborhood hypergraphs and strong hyperedges. Besides, we develop some relations between bipolar fuzzy k-competition hypergraphs and bipolar fuzzy neighborhood hypergraphs. Moreover, authors design an algorithm to compute the strength of competition among companies in business market. A comparative analysis of the proposed model is discuss with the existing models such bipolar fuzzy competition graphs and fuzzy competition hypergraphs.

Dynamic fuzzy neighborhood rough set approach for interval-valued information systems with fuzzy decision

Nowadays, many extended rough set models are proposed to acquire valuable knowledge from interval-valued information system. These existing models mainly focus on different forms of similarity relations. However, most of these similarity relations are qualitative rather than quantitative, which is not reasonable in some practical cases. In addition, with the arrival of new objects and the removal of obsolete objects, the interval-valued information system with fuzzy decision (IvIS_FD) is always changing with time. Therefore, how to efficiently mining knowledge from dynamic IvIS_FD is a meaningful topic. Motivated by these two issues, we study the dynamic fuzzy neighborhood rough set approach for IvIS_FD, aiming to effectively update the rough approximations when the IvIS_FD evolves over time. Firstly, δ-fuzzy neighborhood relation is defined to describe the similarity relation between objects quantitatively. Secondly, we introduce a novel fuzzy neighborhood rough set model and its matrix representation suitable for IvIS_FD. On this basis, we discuss the incremental mechanisms to update fuzzy neighborhood approximations when multiple objects are added to or deleted from an IvIS_FD, respectively. Meanwhile, corresponding dynamic algorithms are designed and explained. Finally, experiments are performed on nine public data sets to evaluate the performance of the dynamic fuzzy neighborhood rough set model. Experimental results verify that the proposed model is effective and efficient for updating rough approximations in dynamic IvIS_FD.

Efficient implementation and parallelization of fuzzy density based clustering

Clustering is a commonly used tool for data management and analysis. One of the prominent group of clustering methods consists of the density-based clustering algorithms. The use of fuzzy neighborhood functions for density-based clustering algorithms are known to significantly improve the robustness, such that choosing neighborhood parameters is rather easy for the user. On the other hand, because of the overhead of the fuzzy calculations, they demand higher computing resources. This study discusses how FN-DNSCAN -a fuzzy density-based clustering algorithm- can be implemented efficiently. A rather specific FN-DBSCAN algorithm that adopts techniques used to improve classical density-based clustering algorithms is introduced. Also, a parallel version of the algorithm is proposed and their implementation details are discussed. The proposed algorithms are tested in a set of comparative experiments, along with a straightforward FN-DBSCAN implementation and a curious but unsafe modification of the parallel algorithm. The results of the experiments that are conducted in a modest parallel computing environment of 32 processing units, show a wide variety of differences in relative speed-ups ranging from 2 to 850 times.