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distributed classification of Multiple Observation Sets by Consensus

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第1期59卷 104-114页

作者： Kokiopoulou, Effrosyni Frossard, Pascal ETH Dept Math Seminar Appl Math CH-8092 Zurich Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 Inst Elect Engn CH-1015 Lausanne Switzerland

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to classify this object by distributed processing in the network. We present a graph-based problem formulation whose objective function captures the smoothness of candidate labels on the data manifold formed by the observations of the object. We design a distributed average consensus algorithm for estimating the unknown object class by computing the value of the smoothness objective function for different class hypotheses. It initially estimates the objective function locally based on the observation of each sensor. As the distributed consensus algorithm progresses, all observations are gradually taken into account in the estimation of the objective function. We illustrate the performance of the distributed classification algorithm for multiview face recognition in an ad hoc network of vision sensors. When the training set is sufficiently large, the simulation results show that the consensus classification decision is equivalent to the decision of a centralized system that has access to all observations.

关键词： distributed classification multiple observation multiview face recognition vision sensor networks

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distributed classification of Gaussian space-time sources in wireless sensor networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2004年第6期22卷 1026-1036页

作者： D'Costa, A Ramachandran, V Sayeed, AM Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

distributed signal processing techniques for classification of objects are studied assuming knowledge of sensor measurement statistics. The spatio-temporal signal field generated by an object is modeled as a bandlimited stationary ergodic Gaussian field. The model. suggests a simple abstraction of correlation between node measurements: it partitions the network into disjoint spatial coherence regions over which the signal remains strongly correlated, whereas the signal in distinct coherence regions is approximately uncorrelated. The size of coherence regions is determined by spatial signal bandwidths. It is shown that this partitioning imposes a structure on optimal distributed classification algorithms that is naturally suited to the communication constraints of the network: local high-bandwidth exchange of feature vectors within each coherence region to improve the measurement signal-to-noise ratio (SNR), and global low-bandwidth exchange of local decisions across coherence regions to stabilize the inherent variability in the signal. Classifier performance is analyzed for both soft and hard decision fusion across coherence regions assuming noise-free, as well as noisy communication links between nodes. Under mild conditions, the probability of error of all classification schemes (soft, hard, noisy) decays exponentially to zero with the number of independent node measurements.-the error exponent depends on both the measurement and communication SNRs and decreases from soft to hard to noisy, fusion. Numerical results based on real data illustrate the remarkable advantage of multiple sensor measurements in distributed decision making.

关键词： Chernoff bounds distributed classification error exponents sensor networks signal modeling

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distributed classification based on distances between probability distributions in feature space

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INFORMATION SCIENCES 2019年 496卷 431-450页

作者： Montero-Manso, Pablo Moran-Fernandez, Laura Bolon-Canedo, Veronica Vilar, Jose A. Alonso-Betanzos, Amparo Univ A Coruna Res Grp Modeling Optimizat & Stat Inference MODES Dept Math Comp Sci Fac La Coruna Spain Univ A Coruna Lab Res & Dev Artificial Intelligence LIDIA Dept Comp Sci Comp Sci Fac La Coruna Spain

We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning algorithm based on combining with different weights the outputs of classifiers trained at each node is proposed. The weights depend on the distributional distance between each node and the test set in the feature space. Two different weighting approaches are introduced, which are referred to as per-Node Weighting (pNW) and per-Instance Weighting (pIW). While pNW assigns the same weight to all test instances at each node, pIW allows distinct weights for test instances differently represented at the node. By construction, our approach is particularly useful to deal with unbalanced nodes. Our methods require no communication between nodes, allowing for data privacy, independence of the kind of trained classifier at each node and maximum training speedup. In fact, our methods do not require retraining of the node's classifiers if available. Although a range of different combination rules are considered to ensemble the single classifiers, theoretical support for the optimality of using the sum rule is provided. Our experiments illustrate all of these properties and show that pIW produces the highest classification accuracies compared with pNW and the standard unweighted approaches. (C) 2019 Elsevier Inc. All rights reserved.

关键词： distributed classification Distributional distances Classifiers combination Imbalanced data set classification accuracy

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distributed classification for image spam detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第11期77卷 13249-13278页

作者： Amir, Amiza Srinivasan, Bala Khan, Asad I. Univ Malaysia Perlis Sch Comp & Commun Engn Arau Perlis Malaysia Monash Univ Fac Informat Technol Melbourne Vic Australia

Spam appears in various forms and the current trend in spamming is moving towards multimedia spam objects. Image spam is a new type of spam attacks which attempts to bypass the spam filters that mostly text-based. Spamming attacks the users in many ways and these are usually countered by having a server to filter the spammers. This paper provides a fully-distributed pattern recognition system within P2P networks using the distributed associative memory tree (DASMET) algorithm to detect spam which is cost-efficient and not prone to a single point of failure, unlike the server-based systems. This algorithm is scalable for large and frequently updated data sets, and specifically designed for data sets that consist of similar occurring *** have evaluated our system against centralised state-of-the-art algorithms (NN, k-NN, naive Bayes, BPNN and RBFN) and distributed P2P-based algorithms (Ivote-DPV, ensemble k-NN, ensemble naive Bayes, and P2P-GN). The experimental results show that our method is highly accurate with a 98 to 99% accuracy rate, and incurs a small number of messages-in the best-case, it requires only two messages per recall test. In summary, our experimental results show that the DAS-MET performs best with a relatively small amount of resources for the spam detection compared to other distributed methods.

关键词： P2P classification distributed pattern recognition Spam detection Image spam distributed classification distributed data mining P2P data mining

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distributed classification in Peer-to-Peer Networks

Distributed Classification in Peer-to-Peer Networks

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13th International Conference on Knowledge Discovery and Data Mining

作者： Luo, Ping Xiong, Hui Lue, Kevin Shi, Zhongzhi Chinese Acad Sci ICT Beijing 100864 Peoples R China Rutgers State Univ New Brunswick NJ 08901 USA Brunel Univ Uxbridge UB8 3PH Middx England

ISBN: (纸本)9781595936097

This work studies the problem of distributed classification in peer-to-peer (P2P) networks. While there has been a significant amount of work in distributed classification, most of existing algorithms are not designed for P2P networks. Indeed, as server-less and router-less systems, P2P networks impose several challenges for distributed classification: (1) it is not practical to have global synchronization in large-scale P2P networks;(2) there are frequent topology changes caused by frequent failure and recovery of peers: and (3) there are frequent on-the-fly data updates on each peer. In this paper, we propose an ensemble paradigm for distributed classification in P2P networks. Under this paradigm each peer builds its local classifiers on the local data and the results from all local classifiers are then combined by plurality voting. To build local classifiers, we adopt the learning algorithm of pasting bites to generate multiple local classifiers on each peer based on the local data. To combine local results, we propose a general form of distributed Plurality Voting (DPV) protocol in dynamic P2P networks. This protocol keeps the single-site validity for dynamic networks, and supports the computing modes of both one-shot query and continuous monitoring. We theoretically prove that the condition C-0 for sending messages used in DPV0 is locally communication-optimal to achieve the above properties. Finally, experimental results on real-world P2P networks show that: (1) the proposed ensemble paradigm is effective even if there are thousands of local classifiers;(2) in most cases, the DPV0 algorithm is local in the sense that voting is processed using information gathered from a very small vicinity, whose size is independent of the network size;(3) DPV, is significantly more communication-efficient than existing algorithms for distributed plurality voting.

关键词： distributed classification P2P networks distributed plurality voting

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distributed classification OF MULTIPLE OBSERVATIONS BY CONSENSUS

DISTRIBUTED CLASSIFICATION OF MULTIPLE OBSERVATIONS BY CONSE...

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IEEE International Conference on Image Processing

作者： Kokiopoulou, Effrosyni Frossard, Pascal ETHZ Seminar Appl Math CH-8092 Zurich Switzerland Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland

ISBN: (纸本)9781424479948

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad-hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to classify this object by distributed processing from the sensors. We present a graph-based problem formulation whose objective function captures the smoothness of candidate labels on the data manifold. We design a distributed average consensus algorithm for estimating the unknown object class by computing the value of the above smoothness objective function for different class hypotheses. It initially estimates the objective function locally, based on the observation of each sensor. All the observations are then progressively taken into account in the estimation of the objective function, along the iterations of the distributed consensus algorithm. We illustrate the performance of the distributed classification algorithm by simulation of multi-view face recognition in an ad-hoc network of vision sensors. When the training set is sufficiently large, the simulation results show that the consensus classification decision is equivalent to the decision of a centralized system that would have access to all observations.

关键词： ad-hoc network computer vision distributed average consensus algorithm distributed classification graph theory graph-based problem formulation image classification multiview face recognition vision sensor

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distributed classification under Statistical Dependence with Application to Automatic Modulation classification 18

Distributed Classification under Statistical Dependence with...

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18th International Conference on Information Fusion (Fusion)

作者： He, Hao Choi, Sora Varshney, Pramod K. Su, Wei Syracuse Univ Dept EECS Syracuse NY 13244 USA US Army Commun Elect RD&E Ctr Aberdeen Proving Ground MD 21005 USA

ISBN: (纸本)9780982443866

In this paper, we consider the distributed classification of discrete random signals in wireless sensor networks (WSNs). Observing the same random signal makes sensors' observations conditionally dependent which complicates the design of distributed classification systems. In the literature, this dependence has been ignored for simplicity although this may significantly affect the performance of the classification system. We derive the necessary conditions for the optimal decision rules at the sensors and the fusion center (FC) by introducing a "hidden" random variable. Furthermore, we introduce an iterative algorithm to search for the optimal decision rules. The proposed scheme is applied to a distributed Automatic Modulation classification (AMC) problem. It is shown to attain superior performance in comparison with other approaches which disregard the inter-sensor dependence.

关键词： distributed classification dependent observations wireless sensor networks automatic modulation classification

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Reliability-Based Adaptive distributed classification in Wireless Sensor Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2010年第9期59卷 4543-4552页

作者： Pai, Hung-Ta Natl Taipei Univ Grad Inst Commun Engn Sanhsia 23741 Taiwan

In many wireless sensor networks, local sensors adopt binary decisions because they can be transmitted to a fusion center using very low power. However, if a binary decision is wrong, the probability of the fusion center making a wrong final decision is dramatically increased. This study proposes a reliability-based adaptive method to resolve this problem with little extra computation. Before a sensor makes a binary local decision, its observation must be evaluated. Unreliable ranges are set for this evaluation. If the sensor's observation result does not fall within the unreliable range, the sensor makes a local decision. Otherwise, the sensor must make another observation. The optimal unreliable ranges are then derived. This study applies the proposed method to an existing distributed classification scheme using the binary decision. Performance analysis shows that this approach efficiently reduces the misclassification probability at the fusion center. Simulation results show that the transmission power is reduced by 7.5 dB to achieve a misclassification probability of 0.1 under some practical conditions.

关键词： Adaptive algorithm binary decision distributed classification reliability wireless sensor networks (WSNs)

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Fault-tolerant distributed classification based on non-binary codes in wireless sensor networks

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IEEE COMMUNICATIONS LETTERS 2005年第9期9卷 808-810页

作者： Wang, TY Han, YSS Varshney, PK Syracuse Univ Dept EECS Syracuse NY 13244 USA

In this letter, we consider the distributed classification problem in wireless sensor networks. The DCSD approach employing the binary code matrix has recently been proposed to cope with the errors caused by both sensor faults and the effect of fading channels. However, the performance of the system employing the binary code matrix could be degraded if the distance between different hypotheses can not be kept large. In this letter, we design the DCSD approach employing the D-ary code matrix when log, D bits local decision information is used, where D > 2. Simulation results show that the performance of the DCSD approach employing the D-ary code matrix is better than that of the DCSD approach employing the binary code matrix.

关键词： fault-tolerance wireless sensor networks coding multisensor fusion distributed classification

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AVERAGING VERSUS VOTING: A COMPARATIVE STUDY OF STRATEGIES FOR distributed classification

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MATHEMATICAL FOUNDATIONS OF COMPUTING 2020年第3期3卷 185-193页

作者： Wang, Donglin Xu, Honglan Wu, Qiang Middle Tennessee State Univ Dept Math Sci 1301 E Main St Murfreesboro TN 37132 USA

In this paper we proposed two strategies, averaging and voting, to implement distributed classification via the divide and conquer approach. When a data set is too big to be processed by one processor or is naturally stored in different locations, the method partitions the whole data into multiple subsets randomly or according to their locations. Then a base classification algorithm is applied to each subset to produce a local classification model. Finally, averaging or voting is used to couple the local models together to produce the final classification model. We performed thorough empirical studies to compare the two strategies. The results show that averaging is more effective in most scenarios.

关键词： distributed classification divide and conquer averaging voting support vector machine logistic regression

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