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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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Simplifying the algorithm selection Using Reduction of Rankings of Classification algorithms 19

Simplifying the Algorithm Selection Using Reduction of Ranki...

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8th International Conference on Software and Computer Applications (ICSCA)

作者： Abdulrahman, Salisu Mamman Brazdil, Pavel Zainon, Wan Mohd Nazmee Wan Adamu, Alhassan Kano Univ Sci & Technol Wudil Kano Nigeria Univ Porto Fac Econ LIAAD INESCTEC Porto Portugal USM Sch Comp Sci George Town Malaysia

ISBN: (纸本)9781450365734

The average ranking method (AR) is one of the simplest and effective algorithms selection methods. This method uses metadata in the form of test results of a given set of algorithms on a given set of datasets and calculates an average rank for each algorithm. The ranks are used to construct the average ranking. In this paper we investigate the problem of how the rankings can be reduced by removing non-competitive and redundant algorithms, thereby reducing the number of tests a user needs to conduct on a new dataset to identify the most suitable algorithm. The method proposed involves two phases. In the first one, the aim is to identify the most competitive algorithms for each dataset used in the past. This is done with the recourse to a statistical test. The second phase involves a covering method whose aim is to reduce the algorithms by eliminating redundant variants. The proposed method differs from one earlier proposal in various aspects. One important one is that it takes both accuracy and time into consideration. The proposed method was compared to the baseline strategy which consists of executing all algorithms from the ranking. It is shown that the proposed method leads to much better performance than the baseline.

关键词： Meta-learning algorithm selection Average Ranking Eliminating non-competitive and redundant algorithms

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Explainable algorithm selection for the Capacitated Lot Sizing Problem 1

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21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR)

作者： Visentin, Andrea Gallchoir, Aodh O. Kaercher, Jens Meyr, Herbert Univ Coll Cork Sch Comp Sci & IT Cork Ireland Insight SFI Res Ctr Data Analyt Cork Ireland Univ Hohenheim Dept Supply Chain Management Stuttgart Germany

ISBN: (数字)9783031605994

ISBN: (纸本)9783031606014;9783031605994

algorithm selection is a class of meta-algorithms that has emerged as a crucial approach for solving complex combinatorial optimization problems. Successful algorithm selection involves navigating a diverse landscape of solvers, each designed with distinct heuristics and search strategies. It is a classification problem in which statistical features of a problem instance are used to select the algorithm that should tackle it most efficiently. However, minimal attention has been given to investigating algorithm selection decisions. This work presents a framework for iterative feature selection and explainable multi-class classification in algorithm selection for the Capacitated Lot Sizing Problem (CLSP). The CLSP is a combinatorial optimization problem widely studied with important industrial applications. The framework reduces the features considered by the machine learning approach and uses SHAP analysis to investigate their contribution to the selection. The analysis shows which instance type characteristics positively affect the relative performance of a heuristic. The approach can be used to improve the algorithm selection's transparency and inform the developer of an algorithm's weak and strong points. The experimental analysis shows that the framework selector provides valuable insights with a narrow optimality gap close to a parallel deployment of the heuristic set that generalises well to instances considerably bigger than the training ones.

关键词： algorithm selection xAI Capacitated Lot Sizing Feature selection SHAP Analysis

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Cost and information-driven algorithm selection for vision systems 1

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International Conference on Image Analysis and Recognition

作者： Marengoni, M Hanson, A Zilberstein, S Riseman, E Marist Coll Dept Comp Sci Poughkeepsie NY 12590 USA Univ Massachusetts Dept Comp Sci Amherst MA 01003 USA

ISBN: (数字)9783540301257

ISBN: (纸本)3540232230

A computer vision application can be defined as a sequence of image processing, feature extraction, and interpretation operations that are used to solve a specific task. In most of the computer vision systems this sequence is pre-defined and static. The work presented here shows a dynamic technique for algorithm selection based on both the value of information of each operation, and the computational complexity of the operations, which is modeled in terms of cost. This technique is used in the Ascender II system to select visual operators to perform aerial image interpretation of urban regions. The results show that the cost and information driven method presented here leads to performance gains. Moreover the hierarchical structure of the system simplifies the addition of new visual operations.

关键词： vision system algorithm selection cost of operators decision making in vision systems

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Analyzing the Generalizability of Automated algorithm selection: A Case Study for Numerical Optimization

Analyzing the Generalizability of Automated Algorithm Select...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Skvorc, Urban Eftimov, Tome Korosec, Peter Jozef Stefan Institute Jozef Stefan International Postgraduate School Computer Systems Department Ljubljana Slovenia Jozef Stefan Institute Computer Systems Department Ljubljana Slovenia

ISBN: (纸本)9781665430654

In numerical single-objective optimization, auto-mated algorithm selection that uses exploratory landscape analy-sis to describe problem features has achieved great results when the machine learning models used for prediction are trained and tested on the same problem set. However, recent work has shown that the performance of such models decreases when the training and testing sets contain different problems. In this paper, we examine a recently developed algorithm selection model trained on a set of artificial problems and tested on a well-known set of hand-made benchmark problems. This model performed poorly when it was originally presented. Here, we provide an explanation for its poor performance by analyzing the feature importance of the model using Shapley Additive Explanations. We then compare these results to an alternative algorithm selection model that was both trained and tested on the same set of hand-made benchmark problems and achieved much higher performance. This allows us to determine which features each model considers as most significant for their predictions, and where they differ. We show that the original and the alternative model use different landscape features for their predictions, which explains the difference in their performance. Further, by plotting the SHAP values on a 2D plane, we show that the original model is unable to distinguish between certain types of problems. Finally, we show that regardless of their differences in utilizing the features both the original and the alternative models perform poorly on a specific group of problems. © 2023 IEEE.

关键词： algorithm selection exploratory landscape analysis numerical optimization

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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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Cross-Disciplinary Perspectives on Meta-Learning for algorithm selection

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ACM COMPUTING SURVEYS 2008年第1期41卷 6-6页

作者： Smith-Miles, Kate A. Monash Univ Sch Math Sci Clayton Vic 3800 Australia

The algorithm selection problem [Rice 1976] seeks to answer the question: Which algorithm is likely to perform best for my problem? Recognizing the problem as a learning task in the early 1990's, the machine learning community has developed the field of meta-learning, focused on learning about learning algorithm performance on classification problems. But there has been only limited generalization of these ideas beyond classification, and many related attempts have been made in other disciplines (such as AI and operations research) to tackle the algorithm selection problem in different ways, introducing different terminology, and overlooking the similarities of approaches. In this sense, there is much to be gained from a greater awareness of developments in meta-learning, and how these ideas can be generalized to learn about the behaviors of other (nonlearning) algorithms. In this article we present a unified framework for considering the algorithm selection problem as a learning problem, and use this framework to tie together the crossdisciplinary developments in tackling the algorithm selection problem. We discuss the generalization of meta-learning concepts to algorithms focused on tasks including sorting, forecasting, constraint satisfaction, and optimization, and the extension of these ideas to bioinformatics, cryptography, and other fields.

关键词： algorithms Performance algorithm selection meta-learning classification forecasting constraint satisfaction combinatorial optimization sorting dataset characterization model selection empirical hardness landscape analysis

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Adaptive local landscape feature vector for problem classification and algorithm selection

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APPLIED SOFT COMPUTING 2022年 131卷

作者： Li, Yaxin Liang, Jing Yu, Kunjie Chen, Ke Guo, Yinan Yue, Caitong Zhang, Leiyu Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China China Univ Min & Technol Beijing 100084 Peoples R China

Fitness landscape analysis is a data-driven technique to study the relationship between problem characteristics and algorithm performance by characterizing the landscape features in the search space of an optimization problem. However, most of the existing landscape features still face poor in classifying the problems and low accuracy in selecting the most appropriate algorithm for a given problem. In this study, an adaptive local landscape feature vector (ALLFV) is proposed for problem classification and algorithm selection. Specifically, an adaptive discretization scheme is designed to calculate adaptive related parameters and construct the sequence between the fitness values of the search point and its nearest neighbors. By considering the frequencies of the same sequence values, the spatial structural information for the fitness landscape is computed as a feature vector according to the feature vector calculation mechanism. The experimental results tested on various problems demonstrate the excellence of ALLFV in terms of accuracy, stability, and computational cost. Moreover, ALLFV has shown superior practicality and reliability in the application of algorithm selection for numerical optimization problems. Consequently, ALLFV is well suited as an alternative for problem classification, as well as algorithm selection under excessive candidate optimization algorithms and limited prior knowledge of problems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Continuous optimization Population-based algorithm Fitness landscape Problem classification algorithm selection

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A conceptual framework for machine learning algorithm selection for predictive maintenance

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Arena, Simone Florian, Eleonora Sgarbossa, Fabio Solvsberg, Endre Zennaro, Ilenia Univ Cagliari Dept Mech Chem & Mat Engn Via Marengo 2 I-09123 Cagliari Italy Univ Padua Dept Management & Engn Stradella San Nicola 3 I-36100 Vicenza Italy NTNU Dept Mech & Ind Engn Richard Birkelands Vei 2B N-7031 Trondheim Norway

The Industry 4.0 paradigm enables advanced data-driven decision-making processes leading many manufacturers to a digital transformation. Within this context, Predictive Maintenance (PdM) - i.e. a maintenance strategy that predicts failures in advance - based on Machine Learning (ML) - i.e. a set of algorithms to analyze data for pattern recognition - emerged as one of the most prominent data-driven analytical approaches for maximizing availability and efficiency of industrial systems. Indeed, there exists a considerable body of literature dealing with ML-based PdM where a wide set of ML algorithms has been applied to a broad range of industrial settings. Whilst this results in extensive knowledge on the topic, the need to choose the right algorithm for a specific task arises as a challenging issue since it is considered an essential stage in the development and implementation of an ML-oriented approach. To respond to such a necessity, this work proposes a conceptual framework to guide practitioners as well as non-expert users in ML algorithm selection for PdM issues. The aim is to provide a set of guidelines and recommendations for the identification of which ML techniques are likely to achieve valuable performance for specific tasks or datasets. First, the most commonly applied ML algorithms in PdM are analyzed together with their core characteristics, advantages, and disadvantages. Then, several decision variables depending on dataset and ML characteristics, learning objectives, accuracy and interpretability are considered. Finally, illustrative case studies are presented to demonstrate how the proposed framework can be adopted in real industrial applications.

关键词： Machine learning Predictive maintenance algorithm selection Conceptual framework Decision making

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Runtime algorithm selection For Grid Environments: A Component Based Framework

Runtime Algorithm Selection For Grid Environments: A Compone...

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作者： Bora, Prachi Virginia Tech | University

Grid environments are inherently heterogeneous. If the computational power provided by collaborations on the Grid is to be harnessed in the true sense, there is a need for applications that can automatically adapt to changes in the execution environment. The application writer should not be burdened with the job of choosing the right algorithm and implementation every time the resources on which the application runs are changed. A lot of research has been done in adapting applications to changing conditions. The existing systems do not address the issue of providing a unified interface to permit algorithm selection at runtime. The goal of this research is to design and develop a unified interface to applications in order to permit seamless access to different algorithms providing similar functionalities. Long running, computationally intensive scientific applications can produce huge amounts of performance data. Often, this data is discarded once the applicationâ s execution is complete. This data can be utilized in extracting information about algorithms and their performance. This information can be used to choose algorithms intelligently. The research described in this thesis aims at designing and developing a component based unified interface for runtime algorithm selection in grid environments. This unified interface is necessary so that the application code does not change if a new algorithm is used to solve the problem. The overhead associated with making the algorithm choice transparent to the application is evaluated. We use a data mining approach to algorithm selection and evaluate its potential effectiveness for scientific applications.

关键词： Common Component Architecture Linear Equations algorithm selection Grid Computing Recommender Systems Thesis

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