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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yang, Zhixiong Bajwa, Waheed U. Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ 08901 USA

ISBN: (纸本)9781479999880

Support vector machines (SVMs) are one of the most widely used supervised learning algorithms for classification problems. Recent years have witnessed an increasing interest in distributed variants of SVMs, in which the (labeled) training data is distributed across different nodes. While a number of algorithms have been developed in this regard, they all make the simplified assumption that every node in the network operates as intended. In many applications, however, it is common for some of the nodes to undergo failures due to faulty equipment, cyber attacks, etc., and inject faulty data into the network. This kind of failure, termed Byzantine failure, is impossible to protect against using existing distributed SVM algorithms. This paper revisits the problem of distributed SVM under the possibility of Byzantine failures in the network. In this regard, it proposes a novel algorithm for distributed SVM that remains resilient to Byzantine failures as long as the number of faulty nodes in the network is not too large. Numerical results on real-world data confirm the superiority of the proposed algorithm over existing approaches.

关键词： Byzantine fault tolerance distributed classification distributed consensus support vector machine

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Computing Massive Trust Analytics for Twitter using Apache Spark with Account Self-assessment 16

Computing Massive Trust Analytics for Twitter using Apache S...

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16th International Conference on Web Information Systems and Technologies (WEBIST)

作者： Drakopoulos, Georgios Kanavos, Andreas Paximadis, Konstantinos Ilias, Aristidis Makris, Christos Mylonas, Phivos Ionian Univ Dept Informat Corfu Greece Univ Patras Comp Engn & Informat Dept Patras Greece Hellen Open Univ Patras Greece

ISBN: (纸本)9789897584787

Although trust is predominantly a human trait, it has been carried over to the Web almost since its very inception. Given the rapid Web evolution to a true melting pot of human activity, trust plays a central role since there is a massive number of parties interested in interacting in a multitude of ways but have little or even no reason to trust a priori each other. This has led to schemes for evaluating Web trust in contexts such as e-commerce, social media, recommender systems. and e-banking. Of particular interest in social networks are classification methods relying on network-dependent attributes pertaining to the past online behavior of an account. Since the deployment of such methods takes place at Internet scale, it makes perfect sense to rely on distributed processing platforms like Apache Spark. An added benefit of distributed platforms is paving the way algorithmically and computationally for higher order Web trust metrics. Here a Web trust classifier in MLlib, the machine learning library for Apache Spark. is presented. It relies on both the account activity but also on that of similar accounts. Three datasets obtained from topic sampling regarding trending Twitter topics serve as benchmarks. Based on the experimental results best practice recommendations are given.

关键词： Higher Order Metrics Web Trust distributed classification MLlib Apache Spark PySpark Twitter

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POWER SCHEDULING FOR distributed MULTIPLE-HYPOTHESIS DETECTION BY TASK-SPECIFIC INFORMATION 5

POWER SCHEDULING FOR DISTRIBUTED MULTIPLE-HYPOTHESIS DETECTI...

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IEEE International Conference on Signal Processing, Communications and Computing ICSPCC 2015

作者： Kim, Hyoung-soo Yang, Sung-Il Hanyang Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781479989201

We introduce a new information theoretic power allocation scheme applicable to distributed multiple-hypothesis detection systems communicating over slow fading channels. In earlier work, it was demonstrated that performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system and the J-divergence method is extended for a distributed multiple hypothesis detection system by defining pairwise sums of the J-divergences. However, the pairwise sum measure does not provide a tight bound. Basically, the more hypotheses we adopt, the less efficient the optimization is. Thus, we derive a more efficient classification-oriented information measure for power optimization of distributed multiple-hypothesis system by introducing a virtual decider variable. The virtual decider variable is directly related with classification task. Various numerical results are also shown to compare the performances.

关键词： optimal power allocation distributed detection multiple hypotheses task-specific information classification distributed classification mutual information slow fading Rayleigh fading channel

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On the discardability of data in Support Vector classification problems

On the discardability of data in Support Vector Classificati...

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50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)

作者： Del Favero, Simone Varagnolo, Damiano Dinuzzo, Francesco Schenato, Luca Pillonetto, Gianluigi Univ. of Padova Italy Max Planck Inst. for Intelligent Systems Germany

ISBN: (纸本)9781612848013

We analyze the problem of data sets reduction for support vector classification. The work is also motivated by distributed problems, where sensors collect binary measurements at different locations moving inside an environment that needs to be divided into a collection of regions labeled in two different ways. The scope is to let each agent retain and exchange only those measurements that are mostly informative for the collective reconstruction of the decision boundary. For the case of separable classes, we provide the exact conditions and an efficient algorithm to determine if an element in the training set can become a support vector when new data arrive. The analysis is then extended to the non-separable case deriving a sufficient discardability condition and a general data selection scheme for classification. Numerical experiments relative to the distributed problem show that the proposed procedure allows the agents to exchange a small amount of the collected data to obtain a highly predictive decision boundary.

关键词： distributed classification support vector machines model reduction distributed machine learning simplex convex analysis

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distributed Pattern Recognition within DHT-Based Networks for Imbalanced Datasets 1

Distributed Pattern Recognition within DHT-Based Networks fo...

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1st IEEE International Conference on Computer Communication and the Internet (ICCCI)

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

ISBN: (纸本)9781467385152

This paper studies the accuracy of a fully-distributed pattern recognition algorithm, namely the P2P-GN, with imbalanced datasets problem which is often neglected by most of the available distributed algorithms. A major distinction of the P2P-GN compared to the other approaches is that it forms a single global classifier, instead of building many local classifiers ( one at every site). Fine-granularity components of the classifier are distributed across the network by using distributed Hash Table (DHT) - which provides efficient linking to these components and ensures the system remains fully-distributed. Our experimental results also show that the P2P-GN can produce highly accurate results despite imbalanced data distribution compared to other distributed algorithms (Ivote-DPV, ensemble ID3, ensemble k-NN, and ensemble naive Bayes).

关键词： P2P-based classification distributed machine learning efficient distributed algorithm distributed classification

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Netted sensors-based vehicle acoustic classification at Tier 1 nodes

Netted sensors-based vehicle acoustic classification at Tier...

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Conference on Unattended Ground Sensor Technologies and Applications VII

作者： Jacyna, GM Christou, CT George, B Necioglu, BF The MITRE Corp. (United States)

ISBN: (纸本)0819457817

The MITRE Corporation has embarked on a three-year internally-funded research program in netted sensors with applications to border monitoring, situational awareness in support of combat identification, and urban warfare. The first-year effort emphasized a border monitoring application for dismounted personnel and vehicle surveillance. This paper will focus primarily on the Tier 1 acoustic-based vehicle classification component. In a hierarchical network topology, distributed clusters of less capable nodes (Tier 1) at the lowest layer are in communication with more capable super nodes (Tier 2) at the next highest layer. Specifically, coarse information is aggregated at the lowest layer and communicated to the next layer in the network where more refined processing and information extraction can take place. We determined that fairly sophisticated classification processing can be performed at a Tier 1 node. This results in sizeable energy savings since data transfer rates are reduced across and between network layers. In general, acoustic-based vehicle classification is a difficult signal processing problem independent of the considerable hardware challenges. The acoustic waveforms are highly non-stationary and lack discernible harmonic structure, particularly for commercial vehicles that are designed to be intentionally quiet. In addition, the waveforms are rather complicated functions of the vehicle speed, engine RPM rate, and sensor-to-vehicle aspect angle. We discuss the development and implementation of a robust linear-weighted classifier on a Mica2 Crossbow mote using feature extraction algorithms specifically developed by MITRE for mote-based processing applications. These include a block floating point Fast Fourier Transform (FFT) algorithm and an 8-band proportional bandwidth filter bank. Results of in-field testing are compared and contrasted with theoretically-derived performance bounds.

关键词： acoustics feature extraction linear discrimination distributed classification Mica2 Crossbow motes

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Global Peer-to-Peer classification in Mobile Ad-Hoc Networks: A Requirements Analysis

Global Peer-to-Peer Classification in Mobile Ad-Hoc Networks...

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7th International and Interdisciplinary Conference on Modeling and Using Context

作者： Gordon, Dawud Scholz, Markus Ding, Yong Beigl, Michael TecO Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783642242786

This paper examines global context classification in peer-to-peer ad-hoc mobile wireless networks (P2P-MANETs). To begin, circumstances are presented in which such systems would be required to classify a global context. These circumstances are expounded upon by presenting concrete scenarios from which a set of requirements are derived. Using these requirements, related work is evaluated for applicability, indicating no adequate solutions. Algorithmic approaches are proposed, and analysis results in a benchmark as well as bounds for distribution of processing load, memory consumption and message passing in P2P-MANETs.

关键词： distributed classification Context Recognition Peer-to-Peer MANET WSN Requirements Analysis

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classification in P2P Networks with Cascade Support Vector Machines

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2013年第4期7卷 20-20页

作者： Ang, Hock Hee Gopalkrishnan, Vivekanand Hoi, Steven C. H. Ng, Wee Keong Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Deloitte Analyt Inst Asia Singapore 068896 Singapore

classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the trade-off between classification accuracy, model construction, and prediction cost. We provide theoretical insights for the proposed solutions and conduct an extensive set of empirical studies on a number of large-scale datasets. Encouraging results validate the efficacy of the proposed approach.

关键词： Design Algorithms Experimentation P2P networks distributed classification cascade SVM bootstrap aggregation

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On the discardability of data in Support Vector classification problems

On the discardability of data in Support Vector Classificati...

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IEEE Conference on Decision and Control

作者： Simone Del Favero Damiano Varagnolo Francesco Dinuzzo Luca Schenato Gianluigi Pillonetto Univ. of Padova Italy Max Planck Inst. for Intelligent Systems Germany

ISBN: (纸本)9781612848006

关键词： distributed classification support vector machines model reduction distributed machine learning simplex convex analysis

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RD-SVM: A RESILIENT distributed SUPPORT VECTOR MACHINE

RD-SVM: A RESILIENT DISTRIBUTED SUPPORT VECTOR MACHINE

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhixiong Yang Waheed U. Bajwa Dept. of Electrical and Computer Engineering Rutgers The State University of New Jersey USA

ISBN: (纸本)9781479999897

关键词： Byzantine fault tolerance distributed classification distributed consensus support vector machine Support Vector Network Resilience Fault tolerance Distribute Ganglia machine failures algorithms

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