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Applying algorithm selection to abductive diagnostic reasoning

APPLIED INTELLIGENCE 2018年第11期48卷 3976-3994页

作者： Koitz-Hristov, Roxane Wotawa, Franz Graz Univ Technol Inst Software Technol Inffeldgasse 16b-2 A-8010 Graz Austria

The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the "best" performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the meta-approach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.

关键词： Abductive reasoning Model-based diagnosis algorithm selection

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Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

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MACHINE LEARNING 2018年第1期107卷 247-283页

作者： Malone, Brandon Kangas, Kustaa Jarvisalo, Matti Koivisto, Mikko Myllymaki, Petri NEC Labs Europe Heidelberg Germany Univ Helsinki HIIT Dept Comp Sci Helsinki Finland

Various algorithms have been proposed for finding a Bayesian network structure that is guaranteed to maximize a given scoring function. Implementations of state-of-the-art algorithms, solvers, for this Bayesian network structure learning problem rely on adaptive search strategies, such as branch-and-bound and integer linear programming techniques. Thus, the time requirements of the solvers are not well characterized by simple functions of the instance size. Furthermore, no single solver dominates the others in speed. Given a problem instance, it is thus a priori unclear which solver will perform best and how fast it will solve the instance. We show that for a given solver the hardness of a problem instance can be efficiently predicted based on a collection of non-trivial features which go beyond the basic parameters of instance size. Specifically, we train and test statistical models on empirical data, based on the largest evaluation of state-of-the-art exact solvers to date. We demonstrate that we can predict the runtimes to a reasonable degree of accuracy. These predictions enable effective selection of solvers that perform well in terms of runtimes on a particular instance. Thus, this work contributes a highly efficient portfolio solver that makes use of several individual solvers.

关键词： Bayesian networks Structure learning algorithm selection Hyperparameter optimization Empirical hardness algorithm portfolio Runtime prediction

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Speeding up algorithm selection using average ranking and active testing by introducing runtime

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MACHINE LEARNING 2018年第1期107卷 79-108页

作者： Abdulrahman, Salisu Mamman Brazdil, Pavel van Rijn, Jan N. Vanschoren, Joaquin Kano Univ Sci & Technol Wudil Kano State Nigeria INESC TEC LIAAD Rua Dr Roberto Frias P-4200465 Oporto Portugal Univ Porto Fac Econ Rua Dr Roberto Frias P-4200465 Oporto Portugal Univ Freiburg Freiburg Germany Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Eindhoven Univ Technol Eindhoven Netherlands

algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.

关键词： algorithm selection Meta-learning Ranking of algorithms Average ranking Active testing Loss curves Mean interval loss

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Analyzing Data Complexity Using Metafeatures for Classification algorithm selection

Analyzing Data Complexity Using Metafeatures for Classificat...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Shah, Rushit N. Khemani, Varun Azarian, Michael H. Pecht, Michael G. Su, Yan Univ Maryland CALCE College Pk MD 20740 USA Nanjing Univ Aeronaut & Astronaut Civil Aviat Coll Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9781538653807

Supervised machine learning is defined as the task of seeking algorithms which, based on reasoning gained from historical data, produce a general hypothesis, can then be used to infer knowledge about new data in the future. Classification, an instance of supervised learning, is statistically defined as the problem of identifying to which particular subpopulation a new observation of data belongs. Given the large number of available classification algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the best method to analyze a new data set has become an ever more challenging task. Typically, finding the best classifier for a given data set involves empirically iterating through all candidate classifiers and choosing the one which provides the best classification accuracies. Clearly, this task is computationally very expensive and the computation cost increases with the addition of each new candidate algorithm. This problem is compounded by the fact that there is not adequate generalizability of these classification methods over data sets from different domains. For example, classifier performance obtained with medical imaging data may not hold for financial data when trying to achieve a similar classification task using the same classifier. How, then, does one efficiently choose a classifier which will provide better classification performance than others? In this context, this study aims to streamline the task of algorithm selection for classification using the meta-learning framework. We propose a methodology to analyze empirically a set of measures of data complexity, known as metafeatures, and investigate their influence on the classification performance of several widely used classifiers. Doing so allows a map of a performance metric to be generated over the metafeature space of data sets. This map is partitioned into regions where some classifiers perform better than others. Once implemented, a new data set can be located in

关键词： metafeatures algorithm selection data complexity

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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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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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Reinforcement-based Method for Simultaneous Clustering algorithm selection and its Hyperparameters Optimization 7

Reinforcement-based Method for Simultaneous Clustering Algor...

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7th Annual International Young Scientists Conference on Computational Science (YSC)

作者： Shalamov, Viacheslav Efimova, Valeria Muravyov, Sergey Filchenkov, Andrey ITMO Univ 49 Kronverksky Pr St Petersburg 197101 Russia

A wide range of clustering algorithms exists, most of them expose many hyperparameters, on which clustering partition quality depends. Simultaneous algorithm (model) selection and its hyperparameters optimization is considered to be a sophisticated task, which is known according to some sources as combined algorithm selection and hyperparameter optimization. In this paper, we focus on problem of selecting a clustering algorithm and its hyperparameter vector simultaneously given a dataset in order to achieve the best partition quality. We propose a method for selecting a proper clustering algorithm and its hyperparameter vector using reinforcement learning. Instead of tuning hyperparameters for all available clustering algorithms and selecting one showing the best performance, we make them to compete for time that they can use for optimizing their own hyperparameters. In our algorithm, we use a framework for multi-armed bandit problem, which is a special case of reinforcement learning. Each clustering algorithm is considered as an arm in the multi-armed bandit setting, while assigning a time budget to optimize hyperparameters of a clustering algorithm is considered as playing the corresponding arm. We conducted series of experiments for comparing out reinforcement learning approach to the classical exhaustive search approach. We conducted experiments on 20 datasets from UCI Repository such as Iris, haberman, krvskp, glass and other. We use 19 cluster validity indices to validate the clusters, built by selected and configured algorithm. As a hyperparameter optimization algorithm, we used SMAC. Our approach managed to improve model selection and hyperparameter optimization process, by sustaining the exploration-exploitation trade-off and spending available time budget more wisely. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： clustering algorithm selection hyperparameter optimization multi-armed bandit reinforcement learning

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Match Field based algorithm selection Approach in Hybrid SUN and PCE Based Optical Networks

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第12期12卷 5723-5743页

作者： Selvaraj, P. Nagarajan, V SRM Univ Sch Comp Kattankulathur India

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment -RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

关键词： Software Defined Networking-SDN algorithm selection Path Computation Element-PCE

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A PAC APPROACH TO APPLICATION-SPECIFIC algorithm selection

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SIAM JOURNAL ON COMPUTING 2017年第3期46卷 992-1017页

作者： Gupta, Rishi Roughgarden, Tim Stanford Univ Dept Comp Sci Stanford CA 94305 USA

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 body of 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-the-art 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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Dynamic algorithm selection for pareto optimal set approximation

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JOURNAL OF GLOBAL OPTIMIZATION 2017年第1-2期67卷 263-282页

作者： Steponavice, Ingrida Hyndman, Rob J. Smith-Miles, Kate Villanova, Laura Monash Univ Sch Math Sci Clayton Vic Australia Monash Univ Dept Econometr & Business Stat Clayton Vic Australia

This paper presents a meta-algorithm for approximating the Pareto optimal set of costly black-box multiobjective optimization problems given a limited number of objective function evaluations. The key idea is to switch among different algorithms during the optimization search based on the predicted performance of each algorithm at the time. algorithm performance is modeled using a machine learning technique based on the available information. The predicted best algorithm is then selected to run for a limited number of evaluations. The proposed approach is tested on several benchmark problems and the results are compared against those obtained using any one of the candidate algorithms alone.

关键词： Multiobjective optimization Expensive black-box function Machine learning Classification algorithm selection Hypervolume metric Features

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