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Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data

Evolutionary-Based Ensemble Under-Sampling for Imbalanced Da...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： YONGQING ZHANG RONGZHAO LU JI HUANG DONGRUI GAO School of Computer Science Chengdu University of Information Technology Chengdu 610225

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. the classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. the advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.

关键词： Classification algorithms Sampling methods Evolutionary computation Machine learning Forestry Information technology Machine learning algorithms

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An Architectural Framework for Serverless Edge Computing: Design and Emulation 10

An Architectural Framework for Serverless Edge Computing: De...

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10th IEEE international conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Cicconetti, Claudio Conti, Marco Passarella, Andrea CNR IIT Pisa Italy

ISBN: (纸本)9781538678992

We consider a Software Defined Networking (SDN)-enabled edge computing domain, where networking devices also have processing capabilities. In particular, we investigate the problem of dynamic allocation of stateless computations, that we call lambda functions, and propose an architectural framework through which requests for execution of lambda functions originated by mobile nodes can be appropriately routed to specific edge devices following a serverless model. In addition, we propose a detailed emulation environment to test the architecture. Our framework supports many possible distributed algorithms to dynamically adapt the choice where requests should be executed, in order to optimize a given performance target. In the paper we consider a few such policies, to test the flexibility of the architecture. We thus present extensive performance results of the considered policies.

关键词： edge computing software defined networking serverless computing computation delegation

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Optimal Multi-access computation Offloading for Mobile Blockchain 16

Optimal Multi-access Computation Offloading for Mobile Block...

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16th IEEE international conference on Communication Systems (IEEE ICCS)

作者： Wu, Yuan Shi, Jiajun Chen, Xiangxu Ni, Kejie Qian, Li Ping Zhang, Kuan Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Univ Nebraska Dept Elect & Comp Engn Lincoln NE USA

ISBN: (纸本)9781538678640

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. the mining process of blockchain, however, requires to consume heavy computation-resources to solve the proof-of-work puzzle, which is prohibitive from the perspective of the conventional mobile terminals (MTs). In this work, we investigate the mobile edge computing (MEC) assisted Blockchain, in which a group of MTs exploit MEC to enhance their computing-powers for solving the proof-of-work puzzle. In particular, we consider the recent advanced multiple-access MEC (MA-MEC) which enables each MT to acquire the computing-powers from several edge servers (ESs) simultaneously. We formulate a joint optimization problem which aims at maximizing the total reward of all MTs, while keeping the fairness among the MTs. Despite the nonconvexity of the problem, we propose efficient layered algorithms to compute the optimal solution. Extensive numerical results have been provided to validate the efficiency of our proposed algorithms.

关键词： Blockchain

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Phase behavior computations using Gibbs free energy minimization on GPUs for speeding up compositional simulations 16

Phase behavior computations using Gibbs free energy minimiza...

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16th European conference on the Mathematics of Oil Recovery, ECMOR 2018

作者： Shiozawa, S. Venkatraman, A. Dindoruk, B. INPEX corporation Japan ReserMine Technology Solutions LLC United States Shell International E and P Inc. United States

ISBN: (纸本)9789462822603

CO2 flooding in a preferred method of enhancing oil recovery as it has the dual benefit of sequestration and increasing oil recovery. In order to evaluate and design these processes, compositional simulations are used to track component changes during the course of flow. However, the phase behavior, as well as the stability computations associated with compositional simulations are time-consuming. In a compositional reservoir simulator, EOS, typically a cubic EOS (i.e., Peng-Robinson) for hydrocarbons, must be solved in all the grid blocks at every time step in order to accurately predict the phase behavior. In typical field-scale simulations, millions of grid blocks are used, and hence solving EOS for multicomponent hydrocarbon systems really slows down the simulation. there is a need to increase the speed and improve the efficiency of phase equilibrium computation for field-scale simulations. In this research, we develop a Gibbs free energy framework optimized for graphic processing unit (GPU) implementation. the Gibbs free energy minimization is preferred as it is a unifying function to combine components described using different thermodynamic models - Equation of State (EOS) as well as activity coefficient models. We use a combination of CPUs and GPUs to solve a constrained minimization problem and the solution is the equilibrium composition at a fixed temperature and pressure (PT flash). the NVIDIA Tesla GPUs help parallelize multiple functions evaluations simultaneously, which results in a significant speedup in computation times. A comparison of computation times of our approach with other approaches to compute and also algorithms to obtain equilibrium compositions is presented -1. CPU versus GPU. 2. K-value versus Gibbs free energy approach. the proposed model can easily be incorporated into existing reservoir simulators to decrease computational times. © 2018 European Association of Geoscientists and Engineers EAGE. All rights reserved.

关键词： Equations of state

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New algorithms for distributed sliding windows 16

New algorithms for distributed sliding windows

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16th Scandinavian Symposium and workshops on Algorithm theory, SWAT 2018

作者： Gayen, Sutanu Vinodchandran, N.V. University of Nebraska-Lincoln LincolnNE United States

ISBN: (纸本)9783959770682

Computing functions over a distributed stream of data is a significant problem with practical applications. the distributed streaming model is a natural computational model to deal with such scenarios. the goal in this model is to maintain an approximate value of a function of interest over a data stream distributed across several computational nodes. these computational nodes have a two-way communication channel with a coordinator node that maintains an approximation of the function over the entire data stream seen so far. the resources of interest, which need to be minimized, are communication (primary), space, and update time. A practical variant of this model is that of distributed sliding window (dsw), where the computation is limited to the last W items, where W is the window size. Important problems such as sampling and counting have been investigated in this model. However, certain problems including computing frequency moments and metric clustering, that are well studied in other streaming models, have not been considered in the distributed sliding window model. We give the first algorithms for computing the frequency moments and metric clustering problems in the distributed sliding window model. Our algorithms for these problems are a result of a general transfer theorem we establish that transforms any algorithm in the distributed infinite window model to an algorithm in the distributed sliding window model, for a large class of functions. In particular, we show an e cient adaptation of the smooth histogram technique of Braverman and Ostrovsky, to the distributed streaming model. Our construction allows trade-o s between communication and space. If we optimize for communication, we get algorithms that are as communication e cient as their infinite window counter parts (upto polylogarithmic factors). © Sutanu Gayen and N. V. Vinodchandran.

关键词： computation theory

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Binary-decomposed DCNN for accelerating computation and compressing model without retraining 16

Binary-decomposed DCNN for accelerating computation and comp...

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16th IEEE international conference on Computer Vision (ICCV)

作者： Kamiya, Ryuji Yamashita, Takayoshi Ambai, Mitsuru Sato, Ikuro Yamauchi, Yuji Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan Denso IT Lab Tokyo Japan

ISBN: (纸本)9781538610343

Recent trends show recognition accuracy increasing even more profoundly. Inference process of Deep Convolutional Neural Networks (DCNN) has a large number of parameters, requires a large amount of computation, and can be very slow. the large number of parameters also require large amounts of memory. this is resulting in increasingly long computation times and large model sizes. To implement mobile and other low performance devices incorporating DCNN, model sizes must be compressed and computation must be accelerated. To that end, this paper proposes Binary-decomposed DCNN, which resolves these issues without the need for retraining. Our method replaces real-valued inner-product computations with binary inner-product computations in existing network models to accelerate computation of inference and decrease model size without the need for retraining. Binary computations can be done at high speed using logical operators such as XOR and AND, together with bit counting. In tests using AlexNet with the ImageNet classification task, speed increased by a factor of 1.79, models were compressed by approximately 80%, and increase in error rate was limited to 1.20%. With VGG-16, speed increased by a factor of 2.07, model sizes decreased by 81%, and error increased by only 2.16%.

关键词： computational modeling Quantization (signal) Acceleration Approximation algorithms Matrix decomposition Image recognition

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Efficient Fitness Function computation of Genetic Algorithm in Virtual Machine Placement for Greener Data Centers

Efficient Fitness Function Computation of Genetic Algorithm ...

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IEEE international conference on Industrial Informatics (INDIN)

作者： Zhe Ding Yu-Chu Tian Maolin Tang School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane QLD Australia

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy efficiency improvement for data centers. Among various methods to solve the VM placement problem, genetic algorithm (GA) has been well accepted for its quality of solutions. However, GA is also computationally demanding, particularly in its fitness, limiting further improvement in energy efficiency of data centers in the scenarios where a fast solution is required. To address this issue, this paper formulates the VM placement problem for energy efficiency as a constrained optimization problem. then, employing GA to solve the optimization, it presents an approach for efficient computation of GA fitness function. the improved computational efficiency is achieved through a new data structure design, which reduces the complexity of the computation from quadratic to linear, to the input size of the problem. Experimental studies show a huge computation time saving from our approach over the existing technique, which is basically Brute-force.

关键词： Genetic algorithms Data centers Mathematical model Energy consumption Data structures Virtual machining Optimization

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Automatic Closed Modeling of Multiple Variable Systems Using Soft computation 16th

Automatic Closed Modeling of Multiple Variable Systems Using...

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16th Mexican international conference on Artificial Intelligence (MICAI)

作者： Kuri-Morales, Angel Cartas-Ayala, Alejandro Inst Tecnol Autonomo Mexico Rio Hondo 1 Mexico City 01000 DF Mexico

ISBN: (纸本)9783030028374;9783030028367

One of the most interesting goals in engineering and the sciences is the mathematical representation of physical, social and other kind of complex phenomena. this goal has been attempted and, lately, achieved with different machine learning (ML) tools. ML owes much of its present appeal to the fact that it allows to model complex phenomena without the explicit definition of the form of the model. Neural networks and support vector machines exemplify such methods. However, in most of the cases, these methods yield "black box" models, i.e. input and output correspond to the phenomena under scrutiny but it is very difficult (or outright impossible) to discern the interrelation of the input variables involved. In this paper we address this problem with the explicit aim of targeting on models which are closed in nature, i.e. the aforementioned relation between variables is explicit. In order to do this, in general, the only assumption regarding the data is that they be approximately continuous. In such cases it is possible to represent the system with polynomial expressions. To be able to do so one must define the number of monomials, the degree of every variable in every monomial and the coefficients associated. We model sparse data systems with an algorithm minimizing the min-max norm. From mathematical and experimental evidence we are able to set a bound on the number of terms and degrees of the approximating polynomials. thereafter, a genetic algorithm (GA) identifies the coefficients which correspond to the terms and degrees defined as above.

关键词： Mathematical modeling Machine learning Multivariate regression Genetic algorithms

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Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: the PAAMS Collection 1st ed. 2018

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丛书名： Communications in Computer and Information Science

2018年

作者： Javier Bajo Juan M. Corchado Elena María Navarro Martínez Eneko Osaba Icedo Philippe Mathieu Patrycja Hoffa-Dąbrowska Elena del Val Sylvain Giroux Antonio J.M. Castro Nayat Sánchez-Pi Vicente Julián Ricardo Azambuja Silveira Alberto Fernández Rainer Unland Rubén Fuentes-Fernández

ISBN: (数字)9783319947792

ISBN: (纸本)9783319947785

this book constitutes the refereed proceedings of the 11 workshops co-located with the 16th international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2018, held in Toledo, Spain, in June 2018. the 47 full papers presented were carefully reviewed and selected from 72 submissions. the volume presents the papers that have been accepted for the following workshops: Workshop on Agents and Multi-agent Systems for AAL and e-HEALth; Workshop on Agent based Applications for Air Transport; Workshop on Agent-based Artificial Markets computational Economics; Workshop on Agent-Based Solutions for Manufacturing and Supply Chain; Workshop on MAS for Complex Networks and Social computation; Workshop on Intelligent Systems and Context Information Fusion; Workshop on Multi-agent based Applications for Energy Markets, Smart Grids and Sustainable Energy Systems; Workshop on Multiagent System based Learning Environments; Workshop on Smart Cities and Intelligent Agents; Workshop on Swarm Intelligence and Swarm Robotics; Workshop on Multi-Agent Systems and Simulation.

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Heterogeneous Parallel and Distributed Optimization of K-means Algorithm on Sunway Supercomputer 15

Heterogeneous Parallel and Distributed Optimization of K-mea...

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15th IEEE international Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Chen, Jiawei Tan, Rong Zhang, Yiwen Washington Univ St Louis MO 63130 USA Cornell Univ Ithaca NY USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781538637906

Clustering plays an essential role in large-volume data analysis areas such as bioinformatics, statistic, pattern recognition and so on. K-means is one of most effective clustering algorithms, which is relatively easy to implement. Most real world applications usually involve a huge amount of data. thus, how to improve applications' efficiency while maintaining accuracy becomes a significant and considerable issue. In this paper, a K-means clustering algorithm, which uses heterogeneous parallel computing technology on Computing processing elements and distributed computing technology, is proposed. this algorithm is applied in unique Sunway architecture based on "Sunway TaihuLight" Supercomputer-the world's fastest supercomputer with peak performance over 100PFLOPS. the testing results suggest that this improved algorithm is stable, fast and efficient. Conclusively, it has a great improvement in computation performance, especially with large volumes of data.

关键词： Clustering algorithms Computer architecture computational modeling Program processors Supercomputers Big Data Data models

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