Fuzzy-rough sets-based feature selection (FRFS), as an effective data pre-processing technique, has drawn significant attention with the growing prevalence of large-scale datasets. However, centralized FRFS approaches suffer from the following shortcomings: 1) low computational efficiency, 2) bottlenecks in memory and computational resources, and 3) strict limitation of collaborative implementation using nonshared datasets owned by different data providers. These limitations highlight the growing necessity of integrating FRFS into a distributed FS framework. Nevertheless, most existing distributed FS schemes are reliant on a designated central server to collect and merge the local results from all slave nodes, which may result in several challenges including single point of failure risk, lack of trust and reliability, and lack of transparency and traceability. To relieve the above issues, this paper proposes a blockchain assisted distributed FS framework, successfully implementing a distributed solution for FRFS (BDFRFS). Firstly, this framework introduces blockchain to merge, reach consensus and publish the global results generated during each iteration of FRFS, including the currently selected feature subset with its corresponding similarity matrix and dependency degree. This not only eliminates the reliance of central server and alleviates the burden on the central server, but also enhances the credibility and traceability of the results. Additionally, the implementation of FRFS is designed within this framework, utilizing three strategies to improve the efficiency of centralized FRFS: 1) eliminating the irrelevant and redundant features prior to the executing FRFS; 2) removing redundant and unnecessary computations involved in generating the similarity matrices; and 3) enabling parallel computation of dependency degrees. Finally, the experimental results conducted on eight large-scale datasets demonstrate that the proposed framework can significantly reduce the runtime cost and improve the classification accuracy compared to centralized FRFS and several distributed FS approaches.

Blockchain, a promising distributed ledger for decentralization, plays a crucial role in JointCloud computing, offering secure data storage and sharing as well as reliable transaction tracking and resource allocation. While promising, it faces storage limitations due to its full-replication strategy. This issue has been addressed by scholars through storage partitioning mechanisms like BFT-Store and PartitionChain. These mechanisms leverage Erasure Coding with the Byzantine Fault Tolerant consensus protocol to overcome storage constraints. However, challenges persist: i) high computational complexity in encoding and decoding leading to prolonged computation time; ii) extensive use of verified signatures causing increased network message transmission and communication burden; iii) during system re-initialization, under-performing scalability and substantial time consumption due to the involving of all nodes in data decoding and re-encoding. To overcome these challenges, we propose two novel storage partitioning mechanisms (i.e., GPartition-Store and ParallelC-Store) for the permissioned blockchain that reduce the computational complexity of the coding processes by dividing nodes into multiple groups called storage units (SUs), avoid additional communication of generating verification proofs by employing the Bloom Filter, and improve the stability and scalability of the system by implementing the re-initialization process exclusively within a specific SU. Particularly, the computational complexity of encoding/decoding can be further degreased by about g2/g3and g/g2compared to PartitionChain, by GPartition-Store and ParallelC-Store respectively (g is the number of SUs). Compared with the full-replication strategy, BFT-Store and PartitionChain, the experimental results demonstrate that the proposed mechanisms improve the Quality of Service (QoS) of the blockchain system including performance (i.e., efficiency and throughput), scalability and stability, while guaranteeing the availability.

Abstract Representation‐based classification (RC) is an effective gauge of data similarity between a single instance and the whole dataset, which extends traditional individual‐wise distance metrics using representation coefficients. These coefficients show remarkable discrimination nature via various regularisation terms, but the interference from potentially uncorrelated objects involved in this single‐to‐global relation can degrade the effectiveness of the coefficients. In order to filter out those unproductive, or even counter‐productive, information from the decision making processes, this paper proposes a local representation‐based classification (LRC) algorithm to improve the classification accuracy or the RC approach. LRC uses a single‐to‐local relation induced by the local representation‐based neighbourhood (LRN) of each object, rather than the single‐to‐global relationship used by RC. Thanks to LRN, a compact and relevant dataset can be formed by selecting the most relevant data instances in the original dataset, to render a robust representation of a query. LRC was applied to multiple publicly available datasets, and the experimental results demonstrate the superiority of the proposed LRC algorithm as evidenced by the higher classification accuracy and more noise‐tolerant capability in reference to alternative RC approaches. Moreover, the sampling ability of LRN is also verified via a comparative study.

Fuzzy-rough sets (FRS) provide an outstanding theoretical tool for feature selection (FS). Whilst promising, the FRS model is sensitive to noisy information and ineffectively applicable to the data with large class density difference, with existing FRS-based FS methods only tackling one of these challenges. Therefore, to overcome both of these issues, this article presents a robust FS algorithm using linear reconstruction measure for the first time. First, a pseudo FRS model is proposed, where the distribution-aware linear reconstruction relation serving as the fuzzy similarity relation is constructed by considering the insight of meaningful information (i.e., distribution information of samples and density information of classes) to enhance the robustness and the pseudofuzzy rough approximations are further redefined based on$k$-Nearest Neighbor ($k$NN) granules determined by the linear reconstruction coefficients to empower the antinoise ability. Then, the pseudo FRS model is employed to guide the robust FS algorithm from the perspective ofredundant filter,strongly relevant priority, anddiscriminative selectionto determine the final feature subset. The experimental results on 31 datasets and practical applications (i.e., cancer diagnosis and face recognition) demonstrate that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRS-based FS and state-of-the-art FS techniques.

Abstract Background The performance of nearest‐neighbour classification is highly sensitive to the quality of data. In order to reduce the impact of the inevitable existence of irrelevant features in data, this paper employs the information learned by neural networks to implement a feature weighting technique and associated weighted multi‐functional nearest‐neighbour classification method. Method The non‐iterative neural networks are easy to implement while enjoying remarkable computational efficiency. In this paper, four non‐iterative neural networks (ELM, E‐ELM, RAWN and RVFL) are employed to evaluate the significance of features to decisions learned and stored in the parameters of neural networks. Moreover, the bias of the significance of features is comforted by using cross‐validation. The resulting feature significance is converted into feature weights to further implement weighted multi‐functional nearest‐neighbour classification. Result The experimental results demonstrate that the proposed neuro‐weighted feature weighting strategy can effectively reduce the impact of irrelevant features and enhance the performance of multi‐functional nearest‐neighbour classification. Contribution The proposed algorithm explores an avenue to efficiently utilize the learned knowledge of neural networks to reduce the role of irrelevant feature in nearest‐neighbour classification.

Fuzzy clustering decomposes data into clusters using partial memberships by exploring the cluster structure information, which demonstrates the comparable performance for knowledge exploitation under the circumstance of information incompleteness. In general, this scheme considers the memberships of objects to cluster centroids and applies to clusters with the spherical distribution. In addition, the noises and outliers may significantly influence the clustering process; a common mitigation measure is the application of separate noise processing algorithms, but this usually introduces multiple parameters, which are challenging to be determined for different data types. This article proposes a new fuzzy-rough intrigued harmonic discrepancy clustering (HDC) algorithm by noting that fuzzy-rough sets offer a higher degree of uncertainty modeling for both vagueness and imprecision present in real-valued datasets. The HDC is implemented by introducing a novel concept of harmonic discrepancy, which effectively indicates the dissimilarity between a data instance and foreign clusters with their distributions fully considered. The proposed HDC is thus featured by a powerful processing ability on complex data distribution leading to enhanced clustering performance, particularly on noisy datasets, without the use of explicit noise handling parameters. The experimental results confirm the effectiveness of the proposed HDC, which generally outperforms the popular representative clustering algorithms on both synthetic and benchmark datasets, demonstrating the superiority of the proposed algorithm.

Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to obtain the highly recognizable representation for the medical images, a self-adaptive discriminative autoencoder (SADAE) is proposed in this paper. The proposed SADAE system is implemented under a deep metric learning framework which consists of$K$local autoencoders, employed to learn the$K$subspaces that represent the diverse distribution of the underlying data, and a global autoencoder to restrict the spatial scale of the learned representation of images. Such community of autoencoders is aided by a self-adaptive metric learning method that extracts the discriminative features to recognize the different categories in the given images. The quality of the extracted features by SADAE is compared against that of those extracted by other state-of-the-art deep learning and metric learning methods on five popular medical image data sets. The experimental results demonstrate that the medical image recognition results gained by SADAE are much improved over those by the alternatives.

Feature extraction plays a vital role in visual action recognition. Many existing gradient-based feature extractors, including histogram of oriented gradients, histogram of optical flow, motion boundary histograms, and histogram of motion gradients, build histograms for representing different actions over the spatio-temporal domain in a video. However, these methods require to set the number of bins for information aggregation in advance. Varying numbers of bins usually lead to inherent uncertainty within the process of pixel voting with regard to the bins in the histogram. This article proposes a novel method to handle such uncertainty by fuzzifying these feature extractors. The proposed approach has two advantages: it better represents the ambiguous boundaries between the bins and, thus, the fuzziness of the spatio-temporal visual information entailed in videos; and the contribution of each pixel is flexibly controlled by a fuzziness parameter for various scenarios. The proposed family of fuzzy descriptors and a combination of them are evaluated on two publicly available datasets, demonstrating that the proposed approach outperforms the original counterparts and other state-of-the-art methods.

The rapid expansion of the Internet has experienced a significant increase in cases of child abuse, as more and more young children have greater access to the Internet. In particular, adults and minors are able to exchange sexually explicit messages and media via a variety of online platforms that are widely available, which leads to an increasing concern of child grooming. Traditionally, the identification of child grooming relies on the analysis and localisation of conversation texts, but this is usually time-consuming and associated with other implications such as psychological pressure on the investigators. Therefore, automatic methods to detect grooming conversations have attracted the attention of many researchers. This paper proposes such a system to identify child grooming in online chat conversations, where the training data of the system were harvested from publicly available information. The data processing is based on a group of AI technologies, including fuzzy-rough feature selection and fuzzy twin support vector machine. Evaluation shows the promise of the proposed approach in identifying online grooming conversations to be implemented in the future after further development to support real-world cases.

CONTEXT AND BACKGROUND: Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution. MOTIVATION: Computer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy. HYPOTHESIS: Use of advanced feature selection methods based on multiple diagnosis criteria may lead to improved results for mammographic risk analysis. METHODS: An approach for multi-criterion based mammographic risk analysis is proposed, by adapting the recently developed multi-label fuzzy-rough feature selection mechanism. RESULTS: A system for multi-criterion mammographic risk analysis is implemented with the aid of multi-label fuzzy-rough feature selection and its performance is positively verified experimentally, in comparison with representative popular mechanisms. CONCLUSIONS: The novel approach for mammographic risk analysis based on multiple criteria helps improve classification accuracy using selected informative features, without suffering from the redundancy caused by such complex criteria, with the implemented system demonstrating practical efficacy.

Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.

The purpose of the intrusion detection systems is to detect attacks on computer systems and networks. Many technologies can be used for intrusion detection, and one of the most effective technologies is data mining. The rapid development of network technology and internet of things makes network intrusion detection become one of the hot topics for research. Various classifiers have been applied in the field of network intrusion detection, but the performance of such approaches highly depends on the features used. Therefore, feature selection approaches have been usually used along with classifiers for network intrusion detection, including the fuzzy-rough feature selection. The fuzzy-rough sets is an extension of the classical rough sets, which can deal with the imprecision and uncertainty of discrete, real value or noise data. It can be seen from the practical applications that there are some shortcomings. Therefore, researchers combine fuzzy-rough sets with kernel methods in order to solve these problems. In this paper, the kernel-based fuzzy-rough feature selection method is used to select the feature subset for the intrusion detection. The proposed approach is validated and evaluated using the KDD 99 dataset with the support of different common classifiers. The experimental outcomes obtained by applying the kernel-based fuzzy-rough feature selection method on KDD data set demonstrate that it performs well in terms of reduction effect and accuracy.

Network intrusion is one of the most serious threats to network infrastructure and digital assets. Intrusion-detection systems detects un-authorized network access which violates the permissions settings of computer systems, networks, and information systems. Data mining is the one of the businesslike techniques amongst which are available for intrusion detection. Many data mining methods, including decision trees and rough set theory, would work better on discrete or binarized data. In data sets, on account of the existence in very large numbers, data discretization is deemed to be the data pretreatment method and such technology is very important. In this paper, the discretization methods of DFSFDP, DBSCAN, CAIM and K-means are implemented and applied to the KDD intrusion detection data set. Then four evaluation indexes are used to evaluate the effect of discretization. Moreover, with regard to the four discretization methods, their performance and accuracy are put forward and contrasted. The results of experiment acquired through using these methods on NSL-KDD dataset prove that used DFSFDP which is selected by the index of inconsistency measure performs well in terms of classification accuracy.

A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi–Sugeno–Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: (1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base and (2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases.

The k nearest-neighbour ( k NN) algorithm has enjoyed much attention since its inception as an intuitive and effective classification method. Many further developments of k NN have been reported such as those integrated with fuzzy sets, rough sets, and evolutionary computation. In particular, the fuzzy and rough modifications of k NN have shown significant enhancement in performance. This paper presents another significant improvement, leading to a multi-functional nearest-neighbour (MFNN) approach which is conceptually simple to understand. It employs an aggregation of fuzzy similarity relations and class memberships in playing the critical role of decision qualifier to perform the task of classification. The new method offers important adaptivity in dealing with different classification problems by nearest-neighbour classifiers, due to the large and variable choice of available aggregation methods and similarity metrics. This flexibility allows the proposed approach to be implemented in a variety of forms. Both theoretical analysis and empirical evaluation demonstrate that conventional k NN and fuzzy nearest neighbour, as well as two recently developed fuzzy-rough nearest-neighbour algorithms can be considered as special cases of MFNN. Experimental results also confirm that the proposed approach works effectively and generally outperforms many state-of-the-art techniques.

The Quality of Services (QoS) is the measure of data transmission quality and service availability of a network, aiming to maintain the data, especially delay-sensitive data such as VoIP, to be transmitted over the network with the required quality. Major network device manufacturers have each developed their own smart dynamic QoS solutions, such as AutoQoS supported by Cisco, CoS (Class of Service) by Netgear devices, and QoS Maps on SROS (Secure Router Operating System) provided by HP, to maintain the service level of network traffic. Such smart QoS solutions usually only work for manufacture qualified devices and otherwise only a pre-defined static policy mapping can be applied. This paper presents a dynamic QoS solution based on the differentiated services (DiffServ) approach for enterprise networks, which is able to modify the priority level of a packet in real time by adjusting the value of Differentiated Services Code Point (DSCP) in Internet Protocol (IP) header of network packets. This is implemented by a 0-order TSK fuzzy model with a sparse rule base which is developed by considering the current network delay, application desired priority level and user current priority group. DSCP values are dynamically generated by the TSK fuzzy model and updated in real time. The proposed system has been evaluated in a real network environment with promising results generated.

Feature selection is a dimensionality reduction technique with the purpose of selecting those input features that are most predictive of a given outcome. The benefits of feature selection include simplified feature internal-relationship and improved prediction performance. Methods based on fuzzy-rough set theory have employed the dependency function to guide the feature selection process with much success. This paper further extends the fuzzy-rough feature selection (FRFS) by using the invasive weed optimisation (IWO) strategy. The advantage of IWO is that the offspring individuals are randomly spread around their parents according to a Gaussian distribution during the evolution process. Such Gaussian distribution is designated with a dynamical standard deviation. Therefore, the optimisation algorithm can explore a new solution space aggressively to maintain the diversity of the species in the early and middle iterations, and the optimal individuals in final iteration of feature selection. The mechanism of IWO ensures a global optimal solution for the heuristic search. The experimental results indicate that the use of IWO in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS with PSO and ACO search strategies, with an increase in overall classification performances.

The Semantic Web is an extension of the current World Wide Web, and aims to help computers to understand and process web information automatically. In recent years, the integration ontologies and rules has become a central topic in the Semantic Web. Therefore, significant research efforts have focused on integration description logic programs. However, description logic programs cannot well model a great deal of real-world problems because of the restriction of represented formalism. To address this problem, we further extend description logic programs such that they can deal with imprecise information, uncertain information and non monotonie reasoning at the same time. In this paper, we propose tightly coupled fuzzy rough description logic programs (or simply fuzzy rough dl-program) under the answer set semantics, which are tightly integrates fuzzy rough disjunctive programs under the answer set semantics with fuzzy rough description logics. To our knowledge, this is the first such approach. First of all, we define the syntax and semantics of fuzzy rough disjunctive logic programs, which is the rough extension of fuzzy disjunctive logic programs based on rough set theory. Then, we define the syntax and semantics of fuzzy rough dl-program. Finally, we show some semantic properties of fuzzy rough dl-program under the answer set semantics.

Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods employs a constraint that enforces the transitivity of the fuzzy T-norm operation. In this paper, such a constraint is relaxed and a new kernel-based fuzzy-rough set approach is introduced. Based on this, novel kernel-based fuzzy-rough nearest-neighbour algorithms are proposed. The work is supported by experimental evaluation, which shows that the new kernel-based methods offer improvements over the existing fuzzy-rough nearest neighbour classifiers. The abstract goes here.