Neighbor Rule Research Articles

Cooperative Learning for Switching Networks With Nonidentical Nonlinear Agents

This article is aimed at realizing cooperative learning for networked multiagent systems subject to uncertain nonlinear dynamics and switching topologies. A distributed control protocol is proposed by integrating the nearest neighbor rules and iterative updating rules. Thanks to cooperative learning, all agents can be ensured to track any prescribed reference robustly over any finite interval, regardless of the nonidentical locally Lipschitz nonlinearities of agents, initial state shifts, and external disturbances. Moreover, a convergence analysis approach to cooperative learning is given by exploring the properties for the products of stochastic matrices that are associated with switching digraphs.

Marketing decision making using sentiment score with co-extracting opinion target words from reviews

This paper proposes a broad-spectrum outline that utilizes natural language processing approaches counting sentiment analysis, web mining that uses clustering schemes to acquire fresh outcome elicited from reviews in support of diverse features of products. An ultimate part in the scheme is finding positive and negative sentiment scope for all features of a product in the given reviews. Massive figures of product reviews are crawling on the Web with the rapid development of Internet. Commencing these reviews, customers can get hold ofactualexamination of product rank and unswerving supervision of their acquisition actions. Withdrawal feelings from web reviews have developed into a gradually more fiery activity and haveengrossed a vastpact of considerationcommencingthe researchers. The key task for opinion mining is obtaining opinion emotions from reviews in online, the novelfactor involves in identifying relations between words. The project proposes a new scheme that contains graph based co-ranking scheme that is employed in finding poise of every word. Then, words with superiorpoise are withdrawn as opinion objects. Based on the nearest neighbor rule we compare with previous schemes, the scheme more preciselycaptures opinion relations, particularly for elongated cover relations. The investigationaloutcome shows that the schemesuccessfully outperforms all other methods and techniques.

Simulating the Expansion of Built-Up Areas using the Models of Logistic Regression, Artificial Neural Network, and Geo-Mod in Marivan City, Iran

Understanding and modeling land use and land cover changes are critically important subjects for the purposes of environmental management, planning, and civil engineering. Models are considered as good approaches for having better estimation or understanding of the phenomenon of land use, land cover changes, thoughtful planning, and alteration in better management of cities and villages. Therefore, this study was conducted to analyze and compare the three models of logistic regression, ANNs, and Geo-Mod in predicting the expansion of built-up areas in a 10-year period in Marivan city, Iran. For this purpose, land cover maps at different times were prepared using Landsat data, including the images of the TM sensor of Landsat 5 in 1989, the ETM+ sensor of Landsat 7 in 2000, and the TM sensor of Landsat 5 in 2011. According to the results, during the period from 1989 to 2011, the built-up area of land cover has been multiplied by 2.74 (73.63 ha/year), which is equal to 1296 ha in 2011 compared to 459 ha in 1989. According to the results, logistic regression has been the preferred method in modeling changes to built-up areas among the presented methods. In addition, one of the advantages of logistic regression is to determine the relationship between variables and changes to the built-up areas. Although Geo-Mod yielded poorer results than the other two methods, its advantages of the neighborhood rule and the maximum use of available data should not be disregarded.

Isolated Sign Language Recognition with Depth Cameras

In this paper, an approach to isolated sign language recognition with data provided by a depth camera is presented. In the introduced method, sequences of depth maps of dynamic sign language gestures are divided into smaller regions (cells). Then, statistical information is used to describe the cells. Since gesture executions have different lengths, the dynamic time warping (DTW) technique with the nearest neighbor (NN) rule is often employed for their comparison. However, due to time-consuming computations, the DTW limits the usability of the classifier. Therefore, in this paper, a selection of representative depth maps using statistics for cells is proposed. It is shown that such gesture representation can be successfully employed for isolated sign language recognition with the NN classifier using the city block distance. Furthermore, the NN rule with the DTW and the introduced statistics for cells provides superior gesture recognition performance.

Entropy Based k Nearest Neighbor Pattern Classification (EbkNN): En-route to Achieving a High Accuracy in Breast Cancer Diagnosis

Entropy based k-Nearest Neighbor pattern classification (EbkNN) is a variation of the conventional k-Nearest Neighbor rule of pattern classification, which exclusively optimizes the value of k-neighbors for each test data based on the calculations of entropy. The formula for entropy used in EbkNN is the one that has been defined popularly in information theory for a set of n different types of information (class) attached to a total of m objects (data points) with each object defined by f features. In EbkNN that value of k is chosen for discrimination of given test data for which the entropy is the least non-zero value. Other rules of conventional kNN are retained in EbkNN. It is concluded that EbkNN works best for binary classification. It is computationally prohibitive to use EbkNN for discriminating the data points of the test dataset into number of classes greater than two. The biggest advantage of EbkNN vis-à-vis the conventional kNN is that in one single run of EbkNN algorithm we get optimum classification of test data. But conventional kNN algorithm has to be run separately for each of the selected range of values of k, and then the optimum k to be chosen from amongst them. We also tested our EbkNN method on WDBC (Wisconsin Diagnostic Breast Cancer) dataset. There are 569 instances in this dataset and we made a random choice of first 290 instances as training dataset and the rest 279 instances as test dataset. We got an exceptionally remarkable result with EbkNN method- accuracy close to 100% and better than the ones got by most of the other researchers who worked on WDBC dataset.

Decentralized aggregation and leader-following control of a swarm of quadcopters with nonlinear under-actuated dynamics

This paper studies the decentralized control of a swarm of quadrotors on the basis of the nonlinear, highly-coupled, and under-actuated dynamic model of quadcopters. The swarm of quadcopters must illustrate the desired swarm behavior as fast as possible while collision avoidance is preserved during the entire evolution process. The interaction relationship among the swarm members is modeled by artificial potential functions and according to the nearest neighbor rule. The sliding mode control technique is employed to control the velocity of quadcopters in the swarm. Finally, two fundamental swarm behaviors, i.e. swarm aggregation and leader-following, are numerically simulated to demonstrate the efficacy of the proposed algorithm.

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.

SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling

Many practical applications suffer from imbalanced data classification, in which case the minority class has degraded recognition rate. The primary causes are the sample scarcity of the minority class and the intrinsic complex distribution characteristics of imbalanced datasets. The imbalanced classification problem is more serious on small sample datasets. To solve the problems of small sample and class imbalance, a hybrid resampling method is proposed. The proposed method combines an oversampling approach (synthetic minority oversampling technique, SMOTE) and a novel data cleaning approach (weighted edited nearest neighbor rule, WENN). First, SMOTE generates synthetic minority class examples using linear interpolation. Then, WENN detects and deletes unsafe majority and minority class examples using weighted distance function and k-nearest neighbor (kNN) rule. The weighted distance function scales up a commonly used distance by considering local imbalance and spacial sparsity. Extensive experiments over synthetic and real datasets validate the superiority of the proposed SMOTE-WENN compared with three state-of-the-art resampling methods.

Distributed Controller Design and Analysis of Second-Order Signed Networks With Communication Delays.

This article concentrates on dealing with distributed control problems for second-order signed networks subject to not only cooperative but also antagonistic interactions. A distributed control protocol is proposed based on the nearest neighbor rules, with which necessary and sufficient conditions are developed for consensus of second-order signed networks whose communication topologies are described by strongly connected signed digraphs. Besides, another distributed control protocol in the presence of a communication delay is designed, for which a time margin of the delay can be determined simultaneously. It is shown that under the delay margin condition, necessary and sufficient consensus results can be derived even though second-order signed networks with a communication delay are considered. Simulation examples are included to illustrate the validity of our established consensus results of second-order signed networks.

Subspace transform induced robust similarity measure for facial images

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.

Recognizing distributions rather than goodness-of-fit testing

This article puts forward an idea of recognizing distributions rather than carrying-out classic goodness-of-fit tests (GoFTs). For the purpose of recognizing the k-nearest neighbors (kNN) rule is applied. We focus the reader’s attention on recognizing the normal distribution. The main part of the article is devoted to the computer implementation of a classifier of distributions that involves kNN rule. GoFTs are conservative. Recognizing distributions is exemplified by simulation and real data examples. When the test statistics exceeds relevant critical value then the verdict sounds: there are reasons to reject And what next? Recognizing distributions is the answer to this question.

Using multiple classifier behavior to develop a dynamic outlier ensemble

Outlier ensembles that use more base detectors recently become an attractive approach to solving problems of single detectors. However, existing outlier ensembles often assume that base detectors make independent errors, which is difficult to satisfy in practical applications. To this end, this paper proposes a dynamic outlier ensemble to loose this error independence assumption. In our method, it is desired that the most competent base detector(s) can be singled out by the dynamic selection mechanism for each test pattern. The usage of the concept of multiple classifier behavior (MCB) has two purposes. One is to generate artificial outlier examples used for competence estimates. This strategy is different from other methods since we do not make any assumption regarding the data distribution. On the other hand, MCB is used to refine validation sets initialized by the K-nearest neighbors (KNN) rule. It is desired that objects in the refined validation sets are more representative than those found by KNN. With the refined validation sets, competences of all base detectors will be estimated by a probabilistic method, before which we have transformed outputs of base detectors into a probabilistic form. Finally, a switching mechanism that determines whether one detector should be nominated to make the decision or a fusion method should be applied instead is proposed in order to achieve a robust detection result. We carry out experiments on 20 benchmark data sets to verify the effectiveness of our detection method.

A New Under-Sampling Method to Face Class Overlap and Imbalance

Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases.

A new locally adaptive [formula omitted]-nearest neighbor algorithm based on discrimination class

The k-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in pattern recognition, and has been widely used in many fields due to its simplicity, effectiveness and intuitiveness. However, the classification performance of the kNN algorithm suffers from the choice of a fixed and single value of k for all queries in the search stage and the use of simple majority voting rule in the decision stage.In this paper, we propose a new kNN-based algorithm, called locally adaptive k-nearest neighbor algorithm based on discrimination class (DC-LAKNN). In our method, the role of the second majority class in classification is for the first time considered. Firstly, the discrimination classes at different values of k are selected from the majority class and the second majority class in the k-neighborhood of the query. Then, the adaptive k value and the final classification result are obtained according to the quantity and distribution information on the neighbors in the discrimination classes at each value of k.Extensive experiments on eighteen real-world datasets from UCI (University of California, Irvine) Machine Learning Repository and KEEL (Knowledge Extraction based on Evolutionary Learning) Repository show that the DC-LAKNN algorithm achieves better classification performance compared to standard kNN algorithm and nine other state-of-the-art kNN-based algorithms.

A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule

Ensembles of outlier detectors are drawing increasing attentions recently, in spite of the difficulty on developing ensembles in the framework of unsupervised learning. We have noted that existing outlier ensembles often use certain fusion rules (e.g. majority voting) to aggregate individual learners. Theoretically, these individuals are assumed to be error-independent so that single models can be outperformed by the ensemble. However, it is of great difficulty to satisfy this assumption in practical applications. By dynamic selecting more competent individual(s) for each test pattern, this problem can be alleviated effectively. Inspired by this idea, this paper proposes a dynamic ensemble outlier detection model using one-class classifiers as base learners. As the competences of base detectors are estimated totally on data points in the validation set, its impact on the selection is significant. In order to achieve an efficient selection, we propose an adaptive k-nearest neighbor (KNN) rule, instead of traditional KNN algorithm, to constitute the validation set for each test pattern. Our adaptive KNN rule firstly uses algorithm support vector data description (SVDD) to mine the local area where class conditional probabilities are not constant in terms of the corresponding test pattern. Competences estimated with neighbor patterns in this area should thus be more accurate than that by traditional KNN rule. A probabilistic model that uses posterior probabilities of one-class classifiers is used then to estimate classifier competences. We present experimental evidence of the detection performance improvement over single models and over a variety of static ensemble models, by using data sets from UCI repository.

Construction and Optimization of Biconnected and Wide-Coverage Topology Based on Node Mobility

Constructing a communications topology with fault tolerance and effective coverage plays an important role in wireless sensor networks. This paper is aimed at constructing and maintaining a biconnected topology, while minimizing the movement distance of the nodes and maximizing the coverage of the field of interest. First, it presents a new model with the motion constraint. If the nodes move at distance within the limit value calculated by the model, the topology is always connected, whether the neighbors of nodes are dynamic or static. Secondly, it improves the coverage strategy based on the nearest neighbor rule (NNR) and finds a rule of nodes’ spreading so that the nodes are distributed evenly and the spacing of the adjacent nodes is controllable. In addition, the nodes move only when necessary according to the added judgment conditions. Consequently, the movement distance is reduced. The simulation results prove the feasibility and effectiveness of the Localized Topology Optimized Method (LTOM) proposed by this paper. The connected indicators of the system’s topology during implementing LTOM are consistent, and the transformation of topology by LTOM is symmetric. Compared with the other distributed algorithm, NNR, LTOM reduces the movement distance of nodes, improves the connected probability, and maximizes the coverage of the topological structures under the biconnected conditions.

Cumulative belief peaks evidential K-nearest neighbor clustering

This paper introduces a new evidential clustering algorithm based on finding the “cumulative belief peaks” and evidential K-nearest neighbor rule. The basic assumption of this algorithm is that a cluster center has the highest cumulative possibility of becoming a cluster center among its neighborhood and size of its neighborhood is relatively large. To measure such cumulative possibility, a new notion of cumulative belief is proposed in the framework of belief functions. By maximizing an objective function, an appropriate size of the relatively large neighborhood is determined. Then, the objects with highest cumulative belief among their own neighborhood of this size are automatically detected as cluster centers. Finally, a credal partition is derived by evidential K-nearest neighbor rule with the fixed cluster center. Experimental results show that the proposed evidential clustering algorithm can automatically detect cluster centers and well reveal the data structure in form of a credal partition in tolerable time, when tackling datasets with small number of data objects and dimensions. As the sizes of datasets increase, running time of such new clustering algorithm increases sharply and this reduces the practicability of it. Simulations on synthetic and real-world datasets validate our conclusions.

Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task.

This paper presents a novel electroencephalography (EEG) evoked paradigm based on neurological rehabilitation. By implementing a conceptual model "cup-and-ball" system, EEG signals in manipulating the dynamic constrained objects are generated. Based on the operational EEG signals, a method is proposed to recognize different mental intentions. Under the manipulating task with a high arousal level, common spatial patterns (CSP) is used to extract and optimize features of the EEG signals from ten participants. Quadratic discriminant analysis (QDA) is implemented on EEG signals in different dimensions to identify different EEG patterns. The cross-validation is used to make classifier adaptive to a given data set. The receiver operating characteristic (ROC) curves are presented to illustrate recognition performance. The classification effect of QDA is verified by paired t-test (P < 0.001). Based on the proposed method, the average accuracy of mental intentions is 0.9857 ± 0.0191 and the area under the ROC curve (AUC) is 0.9665 ± 0.0291. The performance of QDA is also compared with the other three classifiers such as the support vector machine (SVM), the decision tree (DT) and the k-nearest neighborhood (k-NN) rule. The results suggest that the proposed method is very competitive with other methods.

Network-to-Network Control Over Heterogeneous Topologies: A Dynamic Graph Approach

This paper addresses a class of network-to-network control problems in the presence of heterogeneous topologies. To achieve the coordination of nodes in a controlled network, a networked controller with a heterogeneous topology is presented in an observer form based on the nearest neighbor rule. It is shown that the network-to-network control problem can be transformed into a coordination problem on a network subject to hybrid static and dynamic interactions. Furthermore, these hybrid interactions among nodes can be represented by appropriately constructing a dynamic graph, of which the connectivity provides a necessary and sufficient guarantee for all nodes to reach agreement on the average quantity of their initial conditions. Simulations are given to illustrate the effectiveness of our results obtained through the dynamic graph approach.

Unsupervised change detection between SAR images based on hypergraphs

The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection.