Neighbor Selection Strategy Research Articles

Bi-directional adaptive neighborhood rough sets based attribute subset selection

In fields such as pattern recognition and computational intelligence, attribute subset selection has gradually attracted the attention of researchers as a challenging issue, particularly in big data. In rough sets community, many of the selection methods until now have been proposed based on one-directional and non-adaptive rough approximation sets, which increase the uncertainty of granular computing. Based on a series of bi-directional adaptive neighborhood rough sets models, a novel attribute subset selection method is proposed in this paper. In contrast to classical one-directional k-nearest neighborhood relations, the proposed model not only allocates different numbers of neighbors to each sample, but also takes the support information of the neighbors into account in the construction of information granularity. On the basis of set operators, two specific bi-directional neighborhood relations as well as optimistic and pessimistic rough set models are discussed in detail. Several propositions and uncertainty measures not only show the relationship between the traditional and the proposed models, but also present the intrinsic connection of three discussed models. In addition, we propose an optimal neighbor selection scheme, in which justifiable granularity is fused with evidence theory to obtain an optimal k value for each sample. Finally, in the attribute selection process, we prove non-monotonic property of the neighborhood entropy and describe an upgraded forward greedy algorithm. The proposed algorithm is compared with existing algorithms using a ten-fold cross-validation method on several UCI datasets. The experimental results show the advantages of our models in terms of computational performance and classification accuracy, respectively.

Capsule neural tensor networks with multi-aspect information for Few-shot Knowledge Graph Completion

Few-shot Knowledge Graph Completion (FKGC) has recently attracted significant research interest due to its ability to expand few-shot relation coverage in Knowledge Graphs. Prevailing FKGC approaches focus on exploiting the one-hop neighbor information of entities to enhance few-shot relation embedding. However, these methods select one-hop neighbors randomly and neglect the rich multi-aspect information of entities. Although some methods have attempted to leverage Long Short-Term Memory (LSTM) to learn few-shot relation embedding, they are sensitive to the input order. To address these limitations, we propose the Capsule Neural Tensor Networks with Multi-Aspect Information approach (short for InforMix-FKGC). InforMix-FKGC employs a one-hop neighbor selection strategy based on how valuable they are and encodes multi-aspect information of entities, including one-hop neighbors, attributes and literal description. Then, a capsule network is responsible for integrating the support set and deriving few-shot relation embedding. Moreover, a neural tensor network is used to match the query set with the support set. In this way, InforMix-FKGC can learn few-shot relation embedding more precisely so as to enhance the accuracy of FKGC. Extensive experiments on the NELL-One and Wiki-One datasets demonstrate that InforMix-FKGC significantly outperforms ten state-of-the-art methods in terms of Mean Reciprocal Rank and Hits@K.

A novel neighbor selection scheme based on dynamic evaluation towards recommender systems.

Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.

Resilient distributed estimation against FDI attacks: A correntropy-based approach

In this paper, we consider the resilient distributed estimation in adversarial networks, compromised by false data injection (FDI) attacks. We develop a two-step distributed approach to detect the compromised nodes of the network due to FDI attacks. Specifically, we develop a distributed correntropy-based discrimination scheme followed by a distributed state perception scheme to distinguish the compromised nodes from the secure, such that the reliable neighbors could be determined by each node in a distributed manner. We further theoretically analyze the mean and mean-square performance of the resilient distributed estimation, and propose an optimal reference neighbor selection scheme to enhance the network estimation performance. Illustrative simulations validate the superior performance of both FDI attack detection and parameter estimation of the proposed distributed algorithms for adversarial networks, especially under heavy and time-varying FDI attacks. The theoretical results well predict the performance of the proposed resilient distributed estimation algorithm.

Ant-Colony-Algorithm-Based Intelligent Transmission Network Planning

Background: The efficiency of wireless sensor networks is limited by limitations in energy supply. Efficient routing strategies should be designed to save and balance the energy consumption of each node in a wireless sensor network. Aim: In this study, a transmission network based on an ant colony algorithm was proposed to meet the power load demands of a city. Objective: Based on the chaos ant colony algorithm, using a combination of wireless sensor network and node residual energy factors, a neighbor selection strategy was proposed. Results: The optimal result was 1896, and additional lines were: N14-15= 2, N4-16= 1, N5-12= 2, N7-13= L, N6-14= 1, N7-8. The coding method of solving transmission network planning based on multi-stage and multi-dimensional control variables was employed to decompose each control variable into two variables. The sum of total weight and non-zero bits was transformed into high-dimensional variables in state transition probability. Conclusion: The key analysis showed that the ant colony algorithm, as a simulated evolutionary algorithm, is an efficient internal heuristic method.

Multispectral Remote Sensing Data Analysis Based on KNNLC Algorithm and Multimedia Image

In order to combine multimedia imagery and multispectral remote sensing data to analyze information, preprocessing becomes a necessary part of it. It is found that the KNN algorithm is one of the classic algorithms of data mining. As one of the most important branches in the field of data analysis, it is widely used in many fields such as classification, regression, missing value filling, and machine learning. As a lazy algorithm, this method requires no prior statistical knowledge and no additional data to train description rules and is easy to implement. However, the algorithm inevitably has many problems, such as how to determine the appropriate K value, the unsatisfactory effect of data processing for some special distributions, and the unacceptable computational complexity of high-dimensional data. In order to solve these shortcomings, the researchers proposed the KNNLC algorithm. Then, taking the classification experiment as an example, through the comparison of the experimental results on different data sets, it is proved that the average level of the classification performance of the KNNLC algorithm is better than the classic KNN classification algorithm. The KNNLC algorithm shows better performance in most cases, with an accuracy rate of 2 to 5 percentage points higher. An improved algorithm is proposed for the nearest neighbor selection strategy of the traditional KNN algorithm. First, in theory, combined with the theory of sparse coding and locally constrained linear coding, the classical KNN algorithm is improved, and the KNNLC algorithm is proposed. The comparison of the experimental results on the data set proves that the average level of the KNNLC algorithm is better than the classical KNN classification algorithm in terms of classification performance.

A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

In memory-based collaborative filtering (CF) algorithms, the similarity and prediction method have a significant impact on the recommendation results. Most of the existing recommendation techniques have improved different similarity measures to alleviate inaccurate similarity results in sparse data, however, ignored the impact of sparse data on prediction results. To enhance the adaptability to sparse data, we propose a new item-based CF algorithm, which consists of the item similarity measure based vague sets and item-based prediction method with the new neighbor selection strategy. First, in the stage of similarity calculation, the Kullback–Leibler (KL) divergence based on vague sets is proposed from the perspective of user preference probability to measure item similarity. Following this, the impact of rating quantity is further considered to improve the accuracy of similarity results. Next, in the prediction stage, we relax the limit of depending on explicitly ratings and integrate more rating information to adjust prediction results. Experimental results on benchmark data sets show that, compared with other representative algorithms, our algorithm has better prediction and recommendation quality, and effectively alleviates the data sparseness problem.

EGNN: Constructing explainable graph neural networks via knowledge distillation

Graph neural networks are widely utilized for processing data represented by graphs, which renders them ubiquitous in daily life. Due to their excellent performance in extracting features from structural data, graph neural networks have attracted an increasing amount of attention from both academia and industry. Essentially, most GNN models learn representations of nodes by fully/randomly aggregating their neighbor features. However, these unsophisticatedly-designed aggregation schemes always lead to a lack of interpretability, compromising the scope of adoption of GNN models. This study attempts to construct a transparent and explainable GNN model by distilling knowledge from pretrained "black-box" models. Specifically, by simultaneously preserving fidelity to the behaviors of the original model and optimizing the loss of prediction, a shallow graph neural network with explicit "contribution" weights between two nodes is trained. Next, a neighbor selection strategy is built upon these explicit weights to ensure high levels of performance and interpretability. To evaluate the proposed framework, our method is incorporated into four state-of-the-art models: GCN, GAT, GraphSAGE, and AM-GCN. Experimental results on three real-world datasets show the effectiveness of the proposed framework.

ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection

Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However, improper generated minority instances, i.e., noise instances, may interfere the learning of the classifier and impact it negatively. Given this, in this paper, we propose a simple and effective oversampling approach known as ASN-SMOTE based on the k-nearest neighbors and the synthetic minority oversampling technology (SMOTE). ASN-SMOTE first filters noise in the minority class by determining whether the nearest neighbor of each minority instance belongs to the minority or majority class. After that, ASN-SMOTE uses the nearest majority instance of each minority instance to effectively perceive the decision boundary, inside which the qualified minority instances are selected adaptively for each minority instance by the proposed adaptive neighbor selection scheme to synthesize new minority instance. To substantiate the effectiveness, ASN-SMOTE has been applied to three different classifiers and comprehensive experiments have been conducted on 24 imbalanced benchmark datasets. ASN-SMOTE is also extensively compared with nine notable oversampling algorithms. The results show that ASN-SMOTE achieves the best results in the majority of datasets. The ASN-SMOTE implementation is available at: https://www.github.com/yixinkai123/ASN-SMOTE/.

Global P2P BitTorrent Real-Time Traffic Over SDN-Based Local-Aware NG-PON2

File sharing is one of the most common uses in Peer-to-peer (P2P) networks and structured P2P systems, such as BitTorrent (BT), which may overcome the limitations of features such as improved scalability, efficiency, and deterministic data location. Recent research has attempted to reduce inter-ISP P2P traffic by including locality awareness into neighbor-selection strategies of popular P2P apps, which prioritize adjacent peers over distant peers when transferring data, reducing server bandwidth burden, and inter-ISP traffic expense. However, the locality awareness P2P exchanged, handled and coordinated by the network infrastructure to turn around traffic as nearest as possible to the end users was proposed for the passive optical network (PON) because of the large amount of bandwidth in only one single fiber. The PON offers many system architectures; in particular, the NG-PON2 with colorless ONUs, adopting time and wavelength division multiple access technology is often recognized as the best access network solution for simplifying network operation, lowering installation costs, and maintenance costs under control. Software-defined networking (SDN) has sparked interest in various fields as a viable research topic, promising better agility, improved automation, security, and lower capital and operating costs. Taking advantage of the SDN’s OpenFlow protocol in NG-PON2, centralized control renders more flexible control over the BT traffic (P2P intra-traffic) file sharing application provided by different ISPs. Finally, we evaluated our proposed scheme in real-time traffic share collected by Sandvine’s report on 2020 despite the Covid-19 pandemic for different geographic regions, for example; Global, APAC, EMEA, and AMERICA.

Intelligent Control of Swarm Robotics Employing Biomimetic Deep Learning

The collective motion of biological species has robust and flexible characteristics. Since the individual of the biological group interacts with other neighbors asymmetrically, which means the pairwise interaction presents asymmetrical characteristics during the collective motion, building the model of the pairwise interaction of the individual is still full of challenges. Based on deep learning (DL) technology, experimental data of the collective motion on Hemigrammus rhodostomus fish are analyzed to build an individual interaction model with multi-parameter input. First, a Deep Neural Network (DNN) structure for pairwise interaction is designed. Then, the interaction model is obtained by means of DNN proper training. We propose a novel key neighbor selection strategy, which is called the Largest Visual Pressure Selection (LVPS) method, to deal with multi-neighbor interaction. Based on the information of the key neighbor identified by LVPS, the individual uses the properly trained DNN model for the pairwise interaction. Compared with other key neighbor selection strategies, the statistical properties of the collective motion simulated by our proposed DNN model are more consistent with those of fish experiments. The simulation shows that our proposed method can extend to large-scale group collective motion for aggregation control. Thereby, the individual can take advantage of quite limited local information to collaboratively achieve large-scale collective motion. Finally, we demonstrate swarm robotics collective motion in an experimental platform. The proposed control method is simple to use, applicable for different scales, and fast for calculation. Thus, it has broad application prospects in the fields of multi-robotics control, intelligent transportation systems, saturated cluster attacks, and multi-agent logistics, among other fields.

A new globally adaptive k-nearest neighbor classifier based on local mean optimization

The k-nearest neighbor (KNN) rule is a simple and effective nonparametric classification algorithm in pattern classification. However, it suffers from several problems such as sensitivity to outliers and inaccurate classification decision rule. Thus, a local mean-based k-nearest neighbor classifier (LMKNN) was proposed to address these problems, which assigns the query sample with a class label based on the closest local mean vector among all classes. It is proven that the LMKNN classifier achieves better classification performance and is more robust to outliers than the classical KNN classifier. Nonetheless, the unreliable nearest neighbor selection rule and single local mean vector strategy in LMKNN classifier severely have negative effect on its classification performance. Considering these problems in LMKNN, we propose a globally adaptive k-nearest neighbor classifier based on local mean optimization, which utilizes the globally adaptive nearest neighbor selection strategy and the implementation of local mean optimization to obtain more convincing and reliable local mean vectors. The corresponding experimental results conducted on twenty real-world datasets demonstrated that the proposed classifier achieves better classification performance and is less sensitive to the neighborhood size $$k$$ compared with other improved KNN-based classification methods.

Machine Learning based Energy Management at Internet of Things Network Nodes

The Internet of Things networks comprising wireless sensors and controllers or IoT gateways offers extremely high functionalities. However, not much attention is paid towards energy optimization of these nodes and enabling lossless networks. The wireless sensor networks and its applications has industrialized and scaled up gradually with the development of artificial intelligence and popularization of machine learning. The uneven network node energy consumption and local optimum is reached by the algorithm protocol due to the high energy consumption issues relating to the routing strategy. The smart ant colony optimization algorithm is used for obtaining an energy balanced routing at required regions. A neighbor selection strategy is proposed by combining the wireless sensor network nodes and the energy factors based on the smart ant colony optimization algorithm. The termination conditions for the algorithm as well as adaptive perturbation strategy are established for improving the convergence speed as well as ant searchability. This enables obtaining the find the global optimal solution. The performance, network life cycle, energy distribution, node equilibrium, network delay and network energy consumption are improved using the proposed routing planning methodology. There has been around 10% energy saving compared to the existing state-of-the-art algorithms.

Real-time detection of energy consumption of IoT network nodes based on artificial intelligence

Low power loss networks and wireless sensor networks are important components of the Internet of Things. With the development and popularization of artificial intelligence, the network application of wireless sensors is gradually scaled up and industrialized. Aiming at the problems of high energy consumption of the routing strategy, the algorithm protocol is easy to fall into the local optimum and the uneven energy consumption of network nodes. This paper proposes a regional energy balance routing algorithm based on intelligent chaotic ant colony. Based on the intelligent chaotic ant colony algorithm, combined with the remaining energy factors of wireless sensor network nodes, this paper propose a neighbor selection strategy. In order to enhance the ant search ability and speed up the algorithm convergence, an adaptive perturbation strategy and algorithm termination conditions are proposed respectively to find the global optimal solution and avoid falling into the local optimal solution. Simulation results show that the routing planning method in this paper has obvious advantages in terms of network energy consumption and network delay, node equilibrium energy distribution, network life cycle and other performance. Compared with similar algorithms, the average energy consumption has been saved by 7.3%.

A reward based connectivity-aware IoV neighbor selection for improving reliability in healthcare information exchange

Internet of Vehicle (IoV) technology is a promising application of Internet of Things (IoT) employed in smart city environment for smart communication. Traffic handling and non-delay tolerant communication services are expected from the communicating vehicles to improve its application efficiency. With the consideration of IoV in healthcare applications, this manuscript introduces a rapid connection aware neighbor selection (RCNS) for improving the efficiency in handling medical information. The selection process is induced by an instantaneous reward based learning algorithm (RLA) for ease of neighbor selection. The multi-attributes of the neighbors in the information transmitting route are estimated with the knowledge of their position. Distance and reward of the neighbors in the transmission direction are assessed using a beacon-assisted vehicle discovery that minimizes the impact of collision. Different from the other neighbor selection schemes, this method is relies on renewed attribute estimated at the time of transmission. This aids the transmitter to decide transmission and modify transmission attributes. The performance of the proposed neighbor selection method is estimated through extensive simulation using the metrics: delay, delivery ratio, selection overhead and outage.

A Public Goods Game Theory-Based Approach to Cooperation in VANETs Under a High Vehicle Density Condition

There are high demands for cooperation among vehicle nodes to disseminate data in vehicular ad hoc networks (VANETs). Game theory approaches have been applied to analyzing and regulating the behavior of nodes in complex networks. However, game theory-based cooperation in VANETs still poses a research challenge due to continuous changes in the network topology. In this paper, we focus on the issue of the common behavior of cooperation in group vehicular interactions. We first present a dynamic member public goods game (PGG) model for real VANETs in which games are carried out within each group. Then, a dynamic grouping PGG (DGPGG) model is proposed to model VANETs with high-node density. In addition, a greedy neighbor selection strategy is proposed to replace the typical random strategy. Finally, the individual reputation of a vehicle is integrated into the DGPGG model. The simulation results show that the DGPGG model can greatly increase the proportion of cooperative nodes in high-density VANETs no matter whether the network is static or dynamic. When the network topology changes quickly, the greedy neighbor selection strategy can promote cooperation between nodes more effectively than the random strategy.

A Distributed Consensus Protocol Based on Neighbor Selection Strategies for Multi-Agent Systems Convergence

Multi-agent systems (MASs), which consist of numerous mobile agents, is a promising research area in artificial intelligence that has been profusely applied to Engineering. A consensus problem of discrete time MASs with switching topology is investigated in this paper. First, a new distributed consensus protocol based on different neighbor selection strategies is proposed. In order to reach system consensus, the protocol requires each agent to intelligently refer to two neighbors for calculating and updating state. Compared with traditional protocol, the new protocol with different strategies can significantly reduce the cost of comparison, data storage and computation during MASs evolution. Next, three concrete neighbor selection strategies and an optimized strategy are designed. Then, we analyze and prove the stability of the protocol by Lyapunov theorem and Gerschgorin Theorem. The range of parameters, that affect reaching a consensus and equilibrium state in each strategy, is given in the proof. Finally, the experimental results demonstrate the effectiveness of the new protocol and the convergence performance of MASs under different neighbor selection strategies and parameter settings.

A Combined Modified OpportunisticNeighbor Selection and Vehicle Localization Routing Approach for Improved Connectivity in Indian Road Scenario

In the recent years, VANET is becoming a spectacular research area in wireless networks. The high mobility vehicular node in VANET dynamically changes the network topology resulting in highly unstable vehicle connectivity. This induces network partitioning and hence ensuring link availability remains to be a challenging task. To surpass these issues, design of efficient VANET routing algorithms is necessary. The routing design for VANET scenario is highly complex and challenging making the existing AODV, greedy, cluster based routing algorithms to suffer from degraded link quality resulting in high end-to-end delay and significant packet loss. Although Opportunistic Neighbor Selection (ONS) scheme proves to be a better routing logic, it does not seem to always ensure link availability at road intersections, particularly in Indian road scenario, where multi road lane discipline is very hard to implement. To overcome these limitations, a combination of Modified Opportunistic Neighbor Selection (MONS) and Vehicle Localization (VL) routing logic for adoption in Indian road sector has been proposed in this paper. This paper addresses the connectivity challenges and provides better solution to achieve improved performance. In this work, two specific scenarios namely: varied mobility/node density rates is considered by treating the other fixed inorder to evaluate the suitability of the proposed logic in terms of packet delivery ratio, end-to-end delay.

A dynamic trust based two-layer neighbor selection scheme towards online recommender systems

Abstract Collaborative filtering has become one of the most widely used methods for providing recommendations in various online environments. Its recommendation accuracy highly relies on the selection of appropriate neighbors for the target user/item. However, existing neighbor selection schemes have some inevitable inadequacies, such as neglecting users’ capability of providing trustworthy recommendations, and ignoring users’ preference changes. Such inadequacies may lead to drop of the recommendation accuracy, especially when recommender systems are facing the data sparseness issue caused by the dramatic increase of users and items. To improve the recommendation accuracy, we propose a novel two-layer neighbor selection scheme that takes users’ capability and trustworthiness into account. In particular, the proposed scheme consists of two modules: (1) capability module that selects the first layer neighbors based on their capability of providing recommendations and (2) a trust module that further identifies the second layer neighbors based on their dynamic trustworthiness on recommendations. The performance of the proposed scheme is validated through experiments on real user datasets. Compared to three existing neighbor selection schemes, the proposed scheme consistently achieves the highest recommendation accuracy across data sets with different degrees of sparseness.

Cooperative ant colony-genetic algorithm based on spark

By taking full advantages of both the map and reduce function for the MapReduce parallel framework and the memory computation for the Spark platform, this paper designs and implements the algorithms for solving the traveling salesman problem based on ant colony algorithm on MapReduce framework and Spark platform. Next, adds the nearest neighbor selection strategy for choosing next city for the Spark platform ant colony algorithm, and combines it with genetic algorithm by using the optimal individual between ant colony algorithm and genetic algorithm, in order to update each other's best individual at the end of each iteration. Experimental results show that with the increase of ant colony size, compared to the stand-alone ant colony algorithm, MapReduce ant colony algorithm reflects the superiority of parallel computation; compared to the MapReduce ant colony algorithm, Spark platform ant colony algorithm reflects the superiority of memory computing. Cooperated with genetic algorithm, the solution has been improved significantly in its precision.