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检索条件"主题词=Algorithm Design And Analysis"

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An imbalanced data classification method based on automatic clustering under-sampling

An imbalanced data classification method based on automatic ...

引用

IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Xiaoheng Deng Weijian Zhong Ju Ren Detian Zeng Honggang Zhang School of Information Science & Engineering Central South University Changsha China Engineering Department University of Massachusetts Boston

ISBN: (纸本)9781509052530

Classification of imbalanced datasets has become one of the most challenging problems in big data mining. Because the number of positive samples is far less than the negative samples, low accuracy and poor generalization performance and some other defects always go with learning process of traditional algorithms. Ensemble construction algorithm is an important method to handle this problem. Especially, the ensemble construction algorithm based on random under-sampling or clustering can effectively improve the performance of classification. However, the former causes information loss easily and the latter increases complexity. In this paper, we propose ACUS, an improved ensemble algorithm based on automatic clustering and under-sampling. ACUS conducts clustering first according to the weight of samples, and then it constructs balanced-distributed dataset which consists of a certain percentage of the majority class and all of the minority class from each cluster. With Adaboost algorithm construction, these datasets are used to get an ensemble classifier. Experimental results demonstrate the advantages of our proposed algorithm in terms of accuracy, simplicity and high stability.

关键词： Clustering algorithms Classification algorithms Training algorithm design and analysis Boosting Partitioning algorithms Time complexity

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Unsupervised data association for metric learning in the context of multi-shot person re-identification

Unsupervised data association for metric learning in the con...

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Furqan M. Khan François Brémond INRIA Sophia Antipolis-Méditerranée Sophia Antipolis Cedex France

Appearance based person re-identification is a challenging task, specially due to difficulty in capturing high intra-person appearance variance across cameras when inter-person similarity is also high. Metric learning is often used to address deficiency of low-level features by learning view specific re-identification models. The models are often acquired using a supervised algorithm. This is not practical for real-world surveillance systems because annotation effort is view dependent. In this paper, we propose a strategy to automatically generate labels for person tracks to learn similarity metric for multi-shot person re-identification task. We demonstrate on multiple challenging datasets that the proposed labeling strategy significantly improves performance of two baseline methods and the extent of improvement is comparable to that of manual annotations in the context of KISSME algorithm [14].

关键词： Measurement Cameras Training Context Labeling algorithm design and analysis Manuals

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An Evaluation Framework and a Brief Survey of Decision Tree Tools

An Evaluation Framework and a Brief Survey of Decision Tree ...

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International Convention on Information and Communication Technology, Electronics and Microelectronics

作者： N. Vlahovic University of Zagreb Faculty of Economics and Business Zagreb Croatia

ISBN: (纸本)9781509025435

Over the last 50 years decision trees have been extensively used in numerous application areas by practitioners and academics alike. Decision trees are both a mathematical representation tool and a highly versatile graphic representation tool suited for modelling and visualisation of structured hierarchical concepts. Decision tree construction algorithms belong to supervised learning algorithms in the field of machine learning. Most prominent applications areas of decision trees are managerial sciences, knowledge engineering and data analysis. Variety of specific decision tree types is used: decision analysis trees, knowledge representation trees, classification and regression trees, decision forests, etc. Software tools for each of these types are available often borrowing from one decision tree concept to another. As a consequence, distinct features and capabilities of these tools make direct comparison difficult. Goal of this paper is to propose a framework for evaluation of available decision tree tools in relation to their foundation model. Brief survey of currently available decision tree tools will be given accordingly. Results may motivate more comprehensible studies of decision tree applications. Results may also motivate development of hybrid decision tree tools where sound combination of decision tree concepts can enhance functionalities and provide additional benefits to users.

关键词： Decision trees Software tools algorithm design and analysis Analytical models Data models Vegetation

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Methods of testing time-domain simulators in EDA packages

Methods of testing time-domain simulators in EDA packages

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East-West design & Test Symposium (EWDTS)

作者： Alexandr M. Pilipenko Vadim N. Biryukov Anastasia B. Fadeeva Southern Federal University Russia

ISBN: (纸本)9781509006946

This paper presents an original approach to estimate the accuracy of numerical algorithms of transient analysis for four test problems. The tests are presented as mathematical models and electronic circuits too. Test problems and methods of estimation of numerical analysis algorithms' accuracy proposed in the present paper can be used in circuit simulators and in computer mathematics programs to test both known and new simulation algorithms for electronic circuits and systems with various dynamics, including stiff and/or oscillating circuits.

关键词： Oscillators algorithm design and analysis Mathematical model Transient analysis Testing Electronic circuits Integrated circuit modeling

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Compression Acceleration Using GPGPU

Compression Acceleration Using GPGPU

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Krishnaprasad Shastry Avinash Pandey Ashutosh Agrawal Ravi Sarveswara Hewlett-Packard Enterprise

ISBN: (纸本)9781509057740

The CPU has become a bottleneck for bigdata solutions that have to deal with increasingly large and varied data. Two approaches for dealing with this bottleneck have generated considerable interest amongst researchers in the recent past. The first approach is the building of heterogeneous systems that offload a part of the computational workload to co-processors. The offload of a part of the computation to a co-processor causes CPU cycles to be freed up. These freed-up CPU cycles can then be used for running other tasks. Offloading also allows the building of accelerated solutions that run significantly faster. Thus, offloading allows the processing of larger workloads by the system. The second approach that has generated interest is the compression of data. The storage and network latencies for bigdata solutions are reduced significantly when data compression techniques are employed. The associated bandwidth requirements too are reduced, as are the hardware requirements. However, a marriage of the two approaches - compression on a co-processor - has remained a challenge due to the inherent characteristics of the compression algorithms that have made them poor choices for implementation on co-processors such as GPGPUs. In this paper, an implementation of the popular bzip2 compression algorithm that utilizes a GPGPU for a part of its computing is presented. A marginal improvement of 1.4x in performance when compared to the standard CPU-based algorithm is delivered by this implementation, while freeing up CPU cycles by a significant 50% at the same time. This speedup is better than any previously published work. The challenges in getting a good performance for bzip2 on a GPU are discussed.

关键词： Graphics processing units Compression algorithms Transforms algorithm design and analysis Acceleration Data compression Standards

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Performance evaluation of routing algorithms for distributed key-value store based on order preserving linear hashing and Skip graph

Performance evaluation of routing algorithms for distributed...

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International Conference on Computer and Information Science (ACIS)

作者： Ken Higuchi Naoyuki Miyamoto Tatsuo Tsuji Makoto Yoshida Kento Takehara Graduate School of Engineering University of Fukui Fukui Japan Faculty of Engineering University of Fukui Fukui Japan

ISBN: (纸本)9781509008070

In this paper, routing algorithms for the distributed key-value store based on order preserving linear hashing and Skip Graph are evaluated. In this system, data are divided by linear hashing and Skip Graph is used for overlay network. The routing table of this system is very uniform. Then, short detours can exist in the route of forwarding. In this paper, by comparison with an exact shortest route, it proved that the routing algorithm using detour is effective.

关键词： Routing Peer-to-peer computing Arrays algorithm design and analysis Overlay networks Performance evaluation Database systems

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Enhancing learning objects recommendation using multi-criteria recommender systems

Enhancing learning objects recommendation using multi-criter...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Mohammed Hassan Mohamed Hamada Graduate school of computer Science Engineering University of Aizu Aizuwakamatsu Fukushima Japan

ISBN: (纸本)9781509055999

To achieve meaningful learning goals, both pedagogues and tutees need frequent supports on how to obtain relevant materials. Recommendation systems have been proved as important tools that assist learners in getting useful learning objects. Nowadays, various recommendation techniques are used to build a system that can find and suggests learning objects to learners. This paper proposed to use a multi-criteria recommendation technique and aggregation function approach for modeling user preferences on learning objects to improve the quality of recommendations given by the existing traditional recommendation systems. The proposed plan is to develop a neural network model and a hybrid of Genetic and Gradient descent algorithms to train the model using real datasets to learn the behavior of the inputs for accurate predictions of learners' preferences.

关键词： Recommender systems Object oriented modeling Artificial neural networks Mathematical model algorithm design and analysis Prediction algorithms Training

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Functional scheme of the flying sensor networks architecture design

Functional scheme of the flying sensor networks architecture...

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International Conference on Advanced Communication Technology (ICACT)

作者： V. A. Mochalov A. P. Pschenichnikov Institute of Cosmophysical Research and Radio Wave Propagation FEB RAS Mirnaia str. 7 684034 Paratunka Kamchatka region Russia Moscow technical university of communications and informatics Aviamotornaya str. 8a 111024 Moscow Russia

The process of flying sensor networks (FSN) construction is quite complicated. To date, there has been developed a number of methods and algorithms of solving separate problems arising in the process of FSN construction, but the process itself is not formalized as a rigid set of rules, algorithms and standards following which would guarantee construction of a FSN satisfying the designer's requirements. Many problems arising on the mentioned stages of FSN design are NP-complete and cannot be formalized and solved by traditional analytical methods due to fuzziness of task formulation, initial data, criteria and restrictions. In most cases one does not need to derive the optimal solution of the design task; usually the task is reduced to obtaining a spectrum of solutions satisfying design criteria and to selection of an optimal one among them, with interactive participation of the designer in the process of decision making. In this work we propose the functional scheme of the FSN architecture design, which can be the basis for a specialized design support system of flying sensor networks.

关键词： Solid modeling Robot sensing systems Wireless sensor networks Resource management Databases algorithm design and analysis Optimization

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An effective probability distribution SAT solver on reconfigurable hardware

An effective probability distribution SAT solver on reconfig...

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International Conference on Reconfigurable Computing and FPGAs (ReConFig)

作者： Ali Asgar Sohanghpurwala Peter Athanas Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University

Boolean Satisfiability (SAT) is an important problem both theoretically and for a variety of practical applications. While the general SAT problem is NP complete, advanced solver algorithms and heuristics can provide fast and efficient solving of otherwise intractable problems. While much advancement has been made with Conflict Driven Clause Learning (CDCL) based sequential solvers, Stochastic Local Search (SLS) solvers such as WalkSAT, Sparrow and probSAT have proven effective for certain instance types. SLS solvers are well suited to parallelization and hardware implementation due to the simplified control flow and lack of data dependencies between solver instances started with different seeds. This paper presents a hardware implementation of the probSAT algorithm using High-Level Synthesis (HLS) for rapid porting of the design from the original C implementation. Specifically, the presented approach shows very strong performance on the class of small, but difficult SAT problems with speedups between 89-828x over MiniSAT and 5-99x over the software implementation of probSAT on such problems.

关键词： Hardware Field programmable gate arrays algorithm design and analysis Probability distribution Software Benchmark testing Probabilistic logic

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Transformer defect correlation analysis based on Apriori algorithm

Transformer defect correlation analysis based on Apriori alg...

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International Conference on High Voltage Engineering and Application, ICHVE

作者： Yufeng Chen Xiuming Du Liwei Zhou Shandong Electric Power Research Institute Shandong Electric Power Company Jinan Shandong China State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing China

ISBN: (纸本)9781509004973

The association rule from data mining technology was applied into transformer defect analysis so that the frequent pattern, the dependency and the causality between classification and decision attributes could be found based on data of defects. As a result, correlation properties among grid fault elements were seized macroscopically. In this paper which focused on the frequent item mining algorithm research for transformer defect correlation analysis, the definitions related to the association rule were introduced. Specific to weaknesses of traditional Apriori algorithm, an efficient analogous frequent item set mining algorithm was presented. With regard to the instance, association rule analysis was carried out for data of transformer defect in Shandong. Relevant results indicated that diverse attribute items were undoubtedly associated with each other to different degrees; in addition, the correlation obtained was adopted to perform operational maintenance for auxiliary equipment and parts, etc. that are vulnerable to defects.

关键词： algorithm design and analysis Data mining Correlation Oil insulation Transaction databases Partitioning algorithms

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