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检索条件"主题词=Algorithm Selection"

共 312 条记录，以下是81-90 订阅

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Which algorithm Performs Best: algorithm selection for Community Detection 18

Which Algorithm Performs Best: Algorithm Selection for Commu...

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27th World Wide Web (WWW) Conference

作者： Guo, Gaoyang Wang, Chaokun Ying, Xiang Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)9781450356404

A myriad of community detection methods have been designed to discover communities based on specific network features in different disciplines, such as sociology, physics, and computer science. Consequentially, we have to face the problem of algorithm selection for Community Detection (ASCD): Given a specific network, which algorithm should we select to reveal its latent community structures? In this study, we propose a model called CYDES to address the ASCD problem. CYDES consists of two parts, namely feature matrix generation and algorithm classification. We combine three effective feature extraction methods with the idea of BOW model to construct a fixed-size feature matrix. After a nonlinear transformation to the feature matrix, a softmax regression model is utilized to generate a classification label representing the best community detection algorithm we select. Extensive experimental results demonstrate that CYDES has high algorithm selection quality for community detection in networks.

关键词： algorithm selection community detection classification

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A Feature-Free Approach to Automated algorithm selection

A Feature-Free Approach to Automated Algorithm Selection

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Alissa, Mohamad Sim, Kevin Hart, Emma Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9798400701207

This article summarises recent work in the domain of feature-free algorithm selection that was published in the Journal of Heuristics in January 2023, with the title 'Automated algorithm selection: from Feature-Based to Feature-Free Approaches'. Specifically, we consider domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. We train two types of recurrent neural networks (RNN) to predict a packing heuristic in online bin-packing that use the sequence of item-sizes as input, i.e. no features are derived to describe the instance. This contrasts to typical approaches to algorithm-selection. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. In order to explain the observed result, we suggest that our methods perform well when the instances exhibit some implicit structure. To provide evidence for this, 14 new datasets with controllable levels of structure are generated, indicating that a critical threshold of structure is required before algorithm-selection delivers benefit.

关键词： Deep Learning Recurrent Neural Network algorithm selection

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Natural image understanding using algorithm selection and high-level feedback

Natural image understanding using algorithm selection and hi...

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Conference on Intelligent Robots and Computer Vision XXX - algorithms and Techniques

作者： Lukac, Martin Kameyama, Michitaka Hiura, Kosuke Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 980 Japan

ISBN: (纸本)9780819494351

Natural Image processing and understanding encompasses hundreds or even thousands of different algorithms. Each algorithm has a certain peak performance for a particular set of input features and configurations of the objects/regions of the input image (environment). To obtain the best possible result of processing, we propose an algorithm selection approach that permits to always use the most appropriate algorithm for the given input image. This is obtained by at first selecting an algorithm based on low level features such as color intensity, histograms, spectral coefficients. The resulting high level image description is then analyzed for logical inconsistencies (contradictions) that are then used to refine the selection of the processing elements. The feedback created from the contradiction information is executed by a Bayesian Network that integrates both the features and a higher level information selection processes. The selection stops when the high level inconsistencies are all resolved or no more different algorithms can be selected.

关键词： Image Processing Contour extraction algorithm selection Bayesian Networks

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Per-run algorithm selection with Warm-Starting Using Trajectory-Based Features 17th

Per-run Algorithm Selection with Warm-Starting Using Traject...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Kostovska, Ana Jankovic, Anja Vermetten, Diederick de Nobel, Jacob Wang, Hao Eftimov, Tome Doerr, Carola Jozef Stefan Inst Ljubljana Slovenia Jozef Stefan Int Postgrad Sch Ljubljana Slovenia Sorbonne Univ CNRS LIP6 Paris France Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9783031147142;9783031147135

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin as "per-run" algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform.

关键词： algorithm selection Black-box optimization Exploratory landscape analysis Evolutionary computation

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Combinatorial Auction algorithm selection for Cloud Resource Allocation Using Machine Learning 24th

Combinatorial Auction Algorithm Selection for Cloud Resource...

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24th International European Conference on Parallel and Distributed Computing (Euro-Par)

作者： Gudu, Diana Hardt, Marcus Streit, Achim Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9783319969831;9783319969824

Demands for flexibility, efficiency and fine-grained control for the allocation of cloud resources have steered the research in this field towards market-inspired approaches. Combinatorial auctions can fulfill these demands, but their inherent NP-hardness makes them impractical if an optimal solution is desired in a reasonable time. Various heuristic algorithms that yield good allocations fast have been proposed, but their performance and solution quality are highly dependent on the input. In this paper, we investigate which features of a problem instance are predictive of algorithm performance and quality, and propose an algorithm selection method that uses machine learning to find the best heuristic for each given input. We introduce a new cost model for the trade-off between execution time and solution quality, which enables quantitative algorithm comparison. Using feature-based classification to train the algorithm selection model, we can show that our approach outperforms the single best algorithm, as well as a random algorithm selection.

关键词： Cloud resource allocation Combinatorial auction algorithm selection Feature-based classification

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An algorithm selection approach for simulation systems 08

An algorithm selection approach for simulation systems

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22nd International Workshop on Principles of Advanced and Distributed Simulation

作者： Ewald, Roland Himmelspach, Jan Uhrmacher, Adelinde M. Univ Rostock D-18059 Rostock Germany

ISBN: (纸本)9780769531595

No simulation algorithm will deliver best performance under all circumstances, so simulation systems often offer execution alternatives to choose from. This leads to another problem: how is the user supposed to know which algorithm to select? The need for an automated selection mechanism is often neglected, as many simulation systems are focused on specific applications or modeling formalisms and therefore have a limited number of expert users. In general-purpose simulation systems like JAMES II, an 'intelligent' selection mechanism could help to increase the overall performance, especially when users have limited knowledge of the underlying algorithms and their implementation(s) (which is almost always the case). We describe an approach to integrate algorithm selection methods with such systems. Its effectiveness is illustrated in conjunction with the 'plug'n simulate' approach of JAMES II [12].

关键词： simulation performance database performance analysis algorithm selection

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A PAC Approach to Application-Specific algorithm selection 16

A PAC Approach to Application-Specific Algorithm Selection

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7th ACM Conference on Innovations in Theoretical Computer Science (ITCS)

作者： Gupta, Rishi Roughgarden, Tim Stanford Univ 450 Serra Mall Stanford CA 94305 USA

ISBN: (纸本)9781450340571

The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. While there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. This paper adapts concepts from statistical and online learning theory to reason about application-specific algorithm selection. Our models capture several state-of-theart empirical and theoretical approaches to the problem, ranging from self-improving algorithms to empirical performance models, and our results identify conditions under which these approaches are guaranteed to perform well. We present one framework that models algorithm selection as a statistical learning problem, and our work here shows that dimension notions from statistical learning theory, historically used to measure the complexity of classes of binary-and real-valued functions, are relevant in a much broader algorithmic context. We also study the online version of the algorithm selection problem, and give possibility and impossibility results for the existence of no-regret learning algorithms.

关键词： algorithm selection Parameter tuning PAC learning Online learning Meta-algorithms

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Feature selection for SUNNY: a Study on the algorithm selection Library 27

Feature Selection for SUNNY: a Study on the Algorithm Select...

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27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Amadini, Roberto Biselli, Fabio Gabbrielli, Maurizio Liu, Tong Mauro, Jacopo Univ Bologna Dept Comp Sci & Engn I-40126 Bologna Italy INRIA FOCUS Team Paris France Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781509001637

Given a collection of algorithms, the algorithm selection (AS) problem consists in identifying which of them is the best one for solving a given problem. The selection depends on a set of numerical features that characterize the problem to solve. In this paper we show the impact of feature selection techniques on the performance of the SUNNY algorithm selector, taking as reference the benchmarks of the AS library (ASlib). Results indicate that a handful of features is enough to reach similar, if not better, performance of the original SUNNY approach that uses all the available features. We also present sunny-as: a tool for using SUNNY on a generic ASlib scenario.

关键词： algorithm Portfolio algorithm selection Feature selection Machine Learning

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Evaluation of algorithm selection Approach for Semantic Segmentation Based on High-Level Information Feedback

Evaluation of Algorithm Selection Approach for Semantic Segm...

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International Conference on Information and Digital Technologies (IDT)

作者： Lukac, Martin Abdiyeva, Kamila Kameyama, Michitaka Nazarbayev Univ Dept Comp Sci Sch Sci & Technol Astana Kazakhstan Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan

ISBN: (纸本)9781467371865

In this paper we discuss certain theoretical properties of the algorithm selection approach to the problem of semantic segmentation in computer vision. We show that an algorithm's score depends on final task. Thus to properly evaluate an algorithm and to determine its suitability, precise score value obtained on well formulated tasks can be used only. When an algorithm suitability is well known, the algorithm can be efficiently used for a task by applying it in the most favorable environmental conditions determined during the evaluation. However, high quality algorithm selection is possible only if each algorithm suitability is well known because only then the algorithm selection result can improve the best possible result given by a single algorithm. The task dependent evaluation is demonstrated on segmentation and object recognition. Additionally, we also discuss the importance of high level symbolic knowledge in the selection process. The importance of this symbolic hypothesis is demonstrated on a set of learning experiments with both a Bayesian Network and SVM. We show that task dependent evaluation is required to allow efficient algorithm selection. Also by studying symbolic preference of algorithms for semantic segmentation we show that algorithm selection accuracy can be improved by 10 to 15%.

关键词： algorithm selection Semantic Segmentation Machine Learning

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Dynamic Machine Learning algorithm selection For Network Slicing in Beyond 5G Networks 9

Dynamic Machine Learning Algorithm Selection For Network Sli...

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9th IEEE International Conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Bouroudi, Abdelmounaim Outtagarts, Abdelkader Hadjadj-Aoul, Yassine Nokia Networks France Bell Labs Massy France Univ Rennes INRIA CNRS IRISA Rennes France

ISBN: (纸本)9798350399806

The advanced 5G and 6G mobile network generations offer new capabilities that enable the creation of multiple virtual network instances with distinct and stringent requirements. However, the coexistence of multiple network functions on top of a shared substrate network poses a resource allocation challenge known as the Virtual Network Embedding (VNE) problem. In recent years, this NP-hard problem has received increasing attention in the literature due to the growing need to optimize resources at the edge of the network, where computational and storage capabilities are limited. In this demo paper, we propose a solution to this problem, utilizing the algorithm selection (AS) paradigm. This selects the most optimal Deep Reinforcement Learning (DRL) algorithm from a portfolio of agents, in an offline manner, based on past performance. To evaluate our solution, we developed a simulation platform using the OMNeT++ framework, with an orchestration module containerized using Docker. The proposed solution shows good performance and outperforms standalone algorithms.

关键词： Network Slicing Virtual Network Embedding Deep Reinforcement Learning algorithm selection Containerized Networks

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