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检索条件"主题词=Algorithm Selection"

共 313 条记录，以下是91-100 订阅

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Hybrid Ranking and Regression for algorithm selection 43rd

Hybrid Ranking and Regression for Algorithm Selection

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43rd German Conference on Artificial Intelligence (KI)

作者： Hanselle, Jonas Tornede, Alexander Weve, Marcel Huellermeier, Eyke Paderborn Univ Heinz Nixdorf Inst Paderborn Germany Paderborn Univ Dept Comp Sci Paderborn Germany

ISBN: (纸本)9783030582845;9783030582852

algorithm selection (AS) is defined as the task of automatically selecting the most suitable algorithm from a set of candidate algorithms for a specific instance of an algorithmic problem class. While suitability may refer to different criteria, runtime is of specific practical relevance. Leveraging empirical runtime information as training data, the AS problem is commonly tackled by fitting a regression function, which can then be used to estimate the candidate algorithms' runtimes for new problem instances. In this paper, we develop a new approach to algorithm selection that combines regression with ranking, also known as learning to rank, a problem that has recently been studied in the realm of preference learning. Since only the ranking of the algorithms is eventually needed for the purpose of selection, the precise numerical estimation of runtimes appears to be a dispensable and unnecessarily difficult problem. However, discarding the numerical runtime information completely seems to be a bad idea, as we hide potentially useful information about the algorithms' performance margins from the learner. Extensive experimental studies confirm the potential of our hybrid approach, showing that it often performs better than pure regression and pure ranking methods.

关键词： algorithm selection Hybrid loss optimization Combined ranking and regression

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AHP Based Classification algorithm selection for Clinical Decision Support System Development

AHP Based Classification Algorithm Selection for Clinical De...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Khanmohammadi, Sina Rezaeiahari, Mandana SUNY Binghamton Watson Sch Engn Dept Syst Sci & Ind Engn Binghamton NY 13902 USA

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use diftbrent algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest- potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based metalearning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS).The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets. (C) 2014 Published by Elsevier B.V.

关键词： Clinical Decision Support System (CDSS) Machine Leaning Medical informatics algorithm selection AHP Meta-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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A Distance-Weighted Class-Homogeneous Neighbourhood Ratio for algorithm selection 12

A Distance-Weighted Class-Homogeneous Neighbourhood Ratio fo...

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Asian Conference on Machine Learning

作者： Chen, Haofei Liu, Ya Ahuja, Japnit Kaur Ler, Daren Natl Jr Coll 37 Hillcrest Rd Singapore 288913 Singapore Natl Univ Singapore 21 Lower Kent Ridge Rd Singapore 119077 Singapore

In this paper, we introduce a new form of meta-feature that is based on a distance-weighted class-homogeneous neighbourhood ratio to facilitate algorithm selection. We show that these new meta-features, while exhibiting a cost advantage, achieve a comparable, and in some cases, higher performance than conventional meta-features. These results were obtained via experiments conducted over artificial datasets and real-world datasets from the UCI repository. We further redefine the algorithm selection problem by advocating that accuracy should be calculated based on the assumption that the population of datasets is uniformly distributed. Finally, in this paper, we provide a new perspective on landmarkers, such that a landmarker corresponds to a tuple (algorithm, metric), and propose the idea of a new family of meta-features.

关键词： Meta-learning algorithm selection Meta-features Landmarking AutoML

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TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection 21

TRIO: Task-agnostic dataset representation optimized for aut...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Cohen-Shapira, Noy Rokach, Lior Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781665423984

With the growing number of machine learning (ML) algorithms, the selection of the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automatic solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. In this study, we first propose a novel learning-based task-agnostic method for dataset representation. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which is capable of accurately recommending top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation on 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.

关键词： AutoML algorithm selection meta-learning task-agnostic dataset representation

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Reasoning and algorithm selection Augmented Symbolic Segmentation

Reasoning and Algorithm Selection Augmented Symbolic Segment...

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Intelligent Systems Conference (IntelliSys)

作者： Lukac, Martin Abdiyeva, Kamila Kim, Alexandra Kameyama, Michitaka Nazarbayev Univ Dept Comp Sci Astana Kazakhstan Ishinomaki Senshu Univ Dept Comp Sci Ishinomaki Miyagi Japan

ISBN: (纸本)9781509064359

In this paper an alternative method to symbolic segmentation is studied. Semantic segmentation being one of the most difficult tasks currently in the computer vision area, and large number of algorithms is being developed. Thus the proposed approach in this paper exploits this large amount of available computational tools by using the algorithm selection approach. That is, let there be a set A of available algorithms for symbolic segmentation, a set of input features F, a set of image attribute A and a selection mechanism S(F, A, A) that selects on a case by case basis the best algorithm. The semantic segmentation is then an optimization process that combines best component segments from multiple results into a single optimal result. The experiments compare three different algorithm selection mechanisms using three selected semantic segmentation algorithms. The results show that using the current state of art algorithms and relatively low accuracy of algorithm selection the accuracy of the semantic segmentation can be improved by 2%.

关键词： algorithm selection semantic segmentation high-level feedback scene understanding

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Cross-domain algorithm selection: algorithm selection across selection Hyper-heuristics

Cross-domain Algorithm Selection: Algorithm Selection across...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Misir, Mustafa Duke Kunshan Univ Div Nat & Appl Sci Kunshan Peoples R China

ISBN: (纸本)9781665487689

The present study introduces algorithm selection on selection hyper-heuristics. Hyper-heuristics are known as problem-independent methods utilized to solve different instances from varying problem domains. In the literature, there has been effective hyper-heuristic designs providing a certain level of generality in problem solving. Still, the relevant existing research indicates that there is no single hyper-heuristic which performs always the best on different problem solving scenarios. algorithm selection has been investigated essentially to address this issue, mainly for the problem-specific algorithms, by automatically identifying the (near) best algorithm(s) for each given problem instance. This paper performs algorithm selection on selection hyper-heuristics, for the first time, delivering crossdomain algorithm selection. For this purpose, a suite of problemindependent features is initially introduced. Then, algorithm selection is examined across 9 single-objective combinatorial optimization problems with 6 online selection hyper-heuristics. The experimental results carried out on these problems indicated that algorithm selection is effective for choosing hyper-heuristics while offering improved generality and robustness.

关键词： algorithm selection selection hyper-heuristics combinatorial optimization

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On algorithm selection, with an application to combinatorial search problems

On algorithm selection, with an application to combinatorial...

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作者： Kotthoff, Lars University of St Andrews

学位级别：博士

The algorithm selection Problem is to select the most appropriate way for solving a problem given a choice of different ways. Some of the most prominent and successful applications come from Artificial Intelligence and in particular combinatorial search problems. Machine Learning has established itself as the de facto way of tackling the algorithm selection Problem. Yet even after a decade of intensive research, there are no established guidelines as to what kind of Machine Learning to use and how. This dissertation presents an overview of the field of algorithm selection and associated research and highlights the fundamental questions left open and problems facing practitioners. In a series of case studies, it underlines the difficulty of doing algorithm selection in practice and tackles issues related to this. The case studies apply algorithm selection techniques to new problem domains and show how to achieve significant performance improvements. Lazy learning in constraint solving and the implementation of the alldifferent constraint are the areas in which we improve on the performance of current state of the art systems. The case studies furthermore provide empirical evidence for the effectiveness of using the misclassification penalty as an input to Machine Learning. After having established the difficulty, we present an effective technique for reducing it. Machine Learning ensembles are a way of reducing the background knowledge and experimentation required from the researcher while increasing the robustness of the system. Ensembles do not only decrease the difficulty, but can also increase the performance of algorithm selection systems. They are used to much the same ends in Machine Learning itself. We finally tackle one of the great remaining challenges of algorithm selection -- which Machine Learning technique to use in practice. Through a large-scale empirical evaluation on diverse data taken from algorithm selection applications in the literature, we esta

关键词： algorithm selection Combinatorial search Constraint programming Satisfiability Machine learning Q325.5K78

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Flexible algorithm selection Framework for Large Scale Metalearning

Flexible Algorithm Selection Framework for Large Scale Metal...

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11th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Santos, Eugene, Jr. Kilpatrick, Alex Nguyen, Hien Gu, Qi Grooms, Andy Poulin, Chris Dartmouth Coll Thayer Sch Engn 8000 Cummings Hall Hanover NH 03755 USA Univ Wisconsin Whitewater Dept Math & Comp Sci 800 W Main Street Whitewater WI 53190 USA

ISBN: (纸本)9780769548807;9781467360579

We are working on the problem of developing a flexible, generic metalearning process that supports algorithm selection based on studying the algorithms' past performance behaviors. State of the art machine learning systems display limitations in that they require a great deal of human supervision to select an effective algorithm with corresponding options for a specific domain. Additionally, very little guidance is available for algorithm-parameter selection and the number of available choices is overwhelming. In this paper, we develop a flexible, large-scale experimental framework for a metacontroller that supports explorations through algorithm-parameter space and recommend algorithm for a given dataset. First, we aim to facilitate an easy to use process to create a search space for algorithm selection by automatically exploring some possible combinations of algorithms and key parameters. Secondly, our goal is to come up with an algorithm recommendation by looking at the past behaviors of related datasets. Our main contribution is the implemented framework itself which is based on the use of a wide variety of strategies to automatically generate a search space and recommend algorithms for a specific dataset. We evaluate our system with 40 major algorithms on 20 datasets from the UCI repository. Each dataset is represented by 25 data characteristics. We generate and run 7510 combinations of algorithm, parameters and datasets. Our experiments show that our framework offers a friendly way of setting up a machine learning experiment while providing accurate ranking of recommended algorithms based on past behaviors. Specifically, 88% of recommended algorithm rankings significantly correlated with the true rankings for a given dataset.

关键词： metalearning experimental study algorithm selection

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Improving Nevergrad's algorithm selection Wizard NGOpt Through Automated algorithm Configuration 17th

Improving Nevergrad's Algorithm Selection Wizard NGOpt Throu...

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

作者： Trajanov, Risto Nikolikj, Ana Cenikj, Gjorgjina Teytaud, Fabien Videau, Mathurin Teytaud, Olivier Eftimov, Tome Lopez-Ibanez, Manuel Doerr, Carola Fac Comp Sci & Engn Skopje North Macedonia Jozef Stefan Inst Ljubljana Slovenia Jozef Stefan Int Postgrad Sch Ljubljana Slovenia Univ Littoral Cote dOpale Dunkerque France Facebook AI Res Paris France Univ Malaga ITIS Software Malaga Spain Sorbonne Univ LIP6 CNRS Paris France

ISBN: (纸本)9783031147142;9783031147135

algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace.

关键词： algorithm configuration algorithm selection Black-box optimization Evolutionary computation

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