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

Procedia Computer Science 2018年 136卷 144-153页

作者： Viacheslav Shalamov Valeria Efimova Sergey Muravyov Andrey Filchenkov ITMO University 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.

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

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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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algorithm selection in bilateral negotiation

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2016年第4期30卷 697-723页

作者： Ilany, Litan Gal, Ya'akov Ben Gurion Univ Negev Dept Informat Syst Engn IL-84105 Beer Sheva Israel

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an "algorithm selection" problem-which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a "meta-agent" that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration-exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that "a little learning goes a long way", de

关键词： Multi-agent negotiation under incomplete information Empirical methods GENIUS framework algorithm selection

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Instance-Based algorithm selection on Quadratic Assignment Problem Landscapes 17

Instance-Based Algorithm Selection on Quadratic Assignment P...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Beham, Andreas Affenzeller, Michael Wagner, Stefan Univ Appl Sci Upper Austria Softwarepk 11 A-4232 Hagenberg Austria Johannes Kepler Univ Linz Inst Formal Models & Verificat Altenberger Str 69 A-4040 Linz Austria

ISBN: (纸本)9781450349390

Among the many applications of fitness landscape analysis a prominent example is algorithm selection. The no-free-lunch (NFL) theorem has put a limit on the general applicability of heuristic search methods. Improved methods can only be found by specialization to certain problem characteristics which limits their application to other problems. This creates a very interesting and dynamic field for algorithm development. However, this also leads to the definition of a large range of different algorithms that are hard to compare exhaustively. An additional challenge is posed by the fact that algorithms have parameters and thus to each algorithm there may be a large number of instances. In this work the application of algorithm selection to problem instances of the quadratic assignment problem (QAP) is discussed.

关键词： fitness landscapes algorithm selection quadratic assignment problem feature extraction

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algorithm selection Using Performance and Run Time Behavior 17th

Algorithm Selection Using Performance and Run Time Behavior

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17th International Conference on Artificial Intelligence - Methodology, Systems and Applications (AIMSA)

作者： Doan, Tri Kalita, Jugal Univ Colorado 1420 Austin Bluffs Pkwy Colorado Springs CO 80918 USA

ISBN: (纸本)9783319447483;9783319447476

In data mining, an important early decision for a user to make is to choose an appropriate technique for analyzing the dataset at hand so that generalizations can be learned. Intuitively, a trial-and-error approach becomes impractical when the number of data mining algorithms is large while experts' advice to choose among them is not always available and affordable. Our approach is based on meta-learning, a way to learn from prior learning experience. We propose a new approach using regression to obtain a ranked list of algorithms based on data characteristics and past performance of algorithms in classification tasks. We consider both accuracy and time in generating the final ranked result for classification, although our approach can be extended to regression problems.

关键词： algorithm selection Meta-learning Regression

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algorithm selection for Classification Problems

Algorithm Selection for Classification Problems

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SAI Computing Conference (SAI)

作者： Pise, Nitin Kulkarni, Parag Coll Engn Dept Comp Engn & Informat Technol Pune Maharashtra India

ISBN: (纸本)9781467384605

A number of algorithms are available in the areas of data mining, machine learning and pattern recognition for solving the same kind of problem. But there is a little guidance for suggesting algorithm to use which gives best results for the problem at hand. This paper shows an approach for solving this problem using meta-learning. The paper uses three types of data characteristics. Simple, information theoretic, and statistical data characteristics are used. Results are generated using nine different algorithms on thirty eight benchmark datasets from UCI repository. The proposed approach uses K-nearest neighbor algorithm for suggesting the suitable algorithm. Classifier accuracy is taken as a basis for recommending the algorithm. By using meta-learning, accurate method can be recommended as per the given data, and cognitive overload for applying each method, comparing with other methods and then selecting the suitable method for use can be reduced. Thus it helps in adaptive learning methods. The experimentation shows that predicted accuracies are matching with the actual accuracies for more than 90 % of the benchmark datasets used. Thus it is concluded that the number of attributes, the number of instances, the number of classes, maximum probability of class and class entropy are playing a major role in classifier accuracy and algorithm selection for thirty eight datasets used for experimentation.

关键词： machine learning data mining meta-learning algorithm selection classifiers

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ASlib: A benchmark library for algorithm selection

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ARTIFICIAL INTELLIGENCE 2016年 237卷 41-58页

作者： Bischl, Bernd Kerschke, Pascal Kotthoff, Lars Lindauer, Marius Malitsky, Yuri Frechette, Alexandre Hoos, Holger Hutter, Frank Leyton-Brown, Kevin Tierney, Kevin Vanschoren, Joaquin Ludwig Maximilians Univ Munchen Munich Germany Univ Munster Munster Germany Univ Freiburg Freiburg Germany Univ British Columbia Vancouver BC V5Z 1M9 Canada Univ Paderborn Paderborn Germany Eindhoven Inst Technol Eindhoven Netherlands IBM Res Armonk NY USA

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in Al. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms. (C) 2016 Elsevier B.V. All rights reserved.

关键词： algorithm selection Machine learning Empirical performance estimation

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ADAPTIVE algorithm selection, WITH APPLICATIONS IN PEDESTRIAN DETECTION 23

ADAPTIVE ALGORITHM SELECTION, WITH APPLICATIONS IN PEDESTRIA...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Zhang, Shu Zhu, Qi Roy-Chowdhury, Amit SONY Elect Inc San Jose CA 95112 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781467399616

Computer vision algorithms are known to be extremely sensitive to the environmental conditions in which the data is captured, e.g., lighting conditions and target density. Tuning of parameters or choosing a completely new algorithm is often needed to achieve a certain performance level. In this paper, we focus on this problem and propose a framework to automatically choose the "best" algorithm-parameter combination (often referred to as the best algorithm for simplicity in this paper) for a certain input data. This necessitates developing a mechanism to switch among different algorithms and parameters as the nature of the input video changes. Specifically, our proposed algorithm calculates a similarity function between a test video segment and a training video segment. Similarity between training and test dataset indicates the same algorithm can be applied to both of them. We design a cost function with this similarity measure and a constraint on the number of switches. In the experiments, we apply our algorithm to the problem of pedestrian detection. We show how to adaptively select among 7 algorithm-parameter combinations and provide promising results on 3 publicly available datasets.

关键词： algorithm selection adaptation pedestrian detection

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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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algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

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INFORMATION SCIENCES 2015年 317卷 224-245页

作者： Munoz, Mario A. Sun, Yuan Kirley, Michael Halgamuge, Saman K. Monash Univ Sch Math Sci Clayton Vic 3800 Australia Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia Univ Melbourne Dept Comp & Informat Syst Parkville Vic 3010 Australia

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice's (1976) selection framework. We describe each of the four component spaces - problem, algorithm, performance and characteristic - in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. (C) 2015 Elsevier Inc. All rights reserved.

关键词： algorithm selection Black-box continuous optimization Empirical performance models Exploratory landscape analysis Performance prediction Problem hardness measures

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