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14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Bischl, Bernd Mersmann, Olaf Trautmann, Heike Preuss, Mike TU Dortmund Dortmund Germany

ISBN: (纸本)9781450311779

The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, especially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB'09/10 workshop.

关键词： algorithm selection evolutionary optimization fitness landscape exploratory landscape analysis BBOB test set benchmarking machine learning

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algorithm selection of MPI Collectives Considering System Utilization

Algorithm Selection of MPI Collectives Considering System Ut...

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29th International Conference on Parallel and Distributed Computing (Euro-Par)

作者： Beni, Majid Salimi Hunold, Sascha Cosenza, Biagio Univ Salerno Dept Comp Sci Salerno Italy TU Wien Fac Informat Vienna Austria

ISBN: (纸本)9783031488023;9783031488030

MPI collective communications play an important role in coordinating and exchanging data among parallel processes in high performance computing. Various algorithms exist for implementing MPI collectives, each of which exhibits different characteristics, such as message overhead, latency, and scalability, which can significantly impact overall system performance. Therefore, choosing a suitable algorithm for each collective operation is crucial to achieve optimal performance. In this paper, we present our experience with MPI collectives algorithm selection on a large-scale supercomputer and highlight the impact of network traffic and system workload as well as other previously-investigated parameters such as message size, communicator size, and network topology. Our analysis shows that network traffic and system workload can make the performance of MPI collectives highly variable and, accordingly, impact the algorithm selection strategy.

关键词： High Performance Computing MPI Collectives Broadcast algorithm selection Tuning

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algorithm selection and Data Utilization in Machine Learning for Medical Imaging Classification

Algorithm Selection and Data Utilization in Machine Learning...

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18th IEEE Long Island Systems, Applications and Technology Conference

作者： Toma, Milan Husain, Gazi NYIT Dept Osteopath Manipulat Med Coll Osteopath Med Old Westbury NY 11568 USA NYIT Dept Anat Coll Osteopath Med Old Westbury NY USA

ISBN: (纸本)9798331506674;9798331506667

The process of selecting an appropriate Machine Learning (ML) algorithm for medical image classification is inherently complex. This complexity arises from the diverse nature of medical conditions and the varying characteristics of imaging data. For instance, the imaging data for a brain tumor would differ significantly from that of a lung nodule, necessitating different ML approaches. Moreover, different ML algorithms may be more suitable for different types of data and medical conditions. For example, Convolutional Neural Networks (CNNs) have shown exceptional performance in image data, making them a popular choice for medical imaging tasks. On the other hand, algorithms like Support Vector Machines (SVMs) or Decision Trees (DTs) might be more suitable for structured, tabular data. The potential transformative impact of ML in healthcare is vast and multi-faceted. It ranges from predicting disease progression, which can help in early intervention and better patient management, to personalizing treatment plans, which can lead to improved patient outcomes. Furthermore, ML can automate routine tasks, such as image analysis or patient triage, thereby reducing the workload of healthcare professionals and allowing them to focus on more complex tasks. The influence of ML on medical imaging is significant. It offers innovative solutions for image analysis and interpretation, which can enhance diagnostic accuracy and efficiency. For instance, ML algorithms can be used for image classification, where images are categorized into different classes representing various medical conditions. They can also be used for image segmentation, where specific regions of interest within an image are identified and separated. Additionally, ML can enhance images, improving their quality and making it easier for healthcare professionals to identify abnormalities. However, a model trained on data from one hospital, or one population group, might not perform well when applied to data from a dif

关键词： machine learning medical imaging algorithm selection real-world data image classification

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algorithm selection for Preferred Extensions Enumeration 5

Algorithm Selection for Preferred Extensions Enumeration

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5th Conference on Computational Models of Argument (COMMA)

作者： Cerutti, Federico Giacomin, Massimiliano Vallati, Mauro Univ Aberdeen Kings Coll Dept Comp Sci Aberdeen AB9 1FX Scotland Univ Brescia Dept Informat Engn I-25121 Brescia Italy Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH W Yorkshire England

ISBN: (纸本)9781614994367;9781614994350

Enumerating semantics extensions in abstract argumentation is generally an intractable problem. For preferred semantics four algorithms have been recently proposed, AspartixM, NAD-Alg, PrefSAT and SCC-P, with significant runtime variations. This work is a first comprehensive exploration of the graph features and of their impact on the execution time of state-of-the-art preferred extensions enumeration algorithms. Following other areas of AI, we exploit empirical performance models, predictive models that relate instance features and algorithms performance. The result is an approach able to select the "best" algorithm for any Dung's argumentation framework with an accuracy, on the average, of the 80%. Moreover, we show that an algorithm selection approach based on classification can select the fastest algorithm in about the double of the number of cases where the most efficient algorithm outperforms the other ones (SCC-P), and about three times the number of cases of the second most efficient algorithm (PrefSAT).

关键词： argumentation semantics argumentation features algorithm selection

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algorithm selection across algorithm Configurators A Case Study on Multi-objective Optimization

Algorithm Selection across Algorithm Configurators A Case St...

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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 work utilizes algorithm selection for automatically specifying the parameter tuning method for a given tuning task. The idea of parameter tuning is motivated by the premature algorithm designs and their sub-optimal or poor parameter value choices. The decisions both on the designs and the parameter values are largely given based on the experiences of the developers or the target problem domain experts after a limited number of trials. While the existing tuning approaches tend to offer improvements over the default parameter values for varying algorithms, they can be computationally expensive. Additionally, there is no a single, ultimate parameter tuning strategy. These facts suggest to choose the most effective tuning algorithm for a specific scenario. This study utilizes an existing algorithm selection system to address this problem. The idea is to allocate potentially the most effective tuning method for a given task instead of relying on a single tuner. On that note, a group of well-known parameter configuration approaches are accommodated as the candidate methods to tune NSGA-II for solving a suite of multi-objective optimization benchmarks, referencing a recent article. The computational analysis revealed that algorithm selection outperforms those constituent parameter tuning methods when each is used as a standalone manner. Additionally, the dis/-similarity analysis carried on the problem instances / benchmarks give hints on the diversity level of the benchmarks. Furthermore, a similar inspection reported on the parameter tuning procedures show the behavioural resemblance between them.

关键词： algorithm selection algorithm configuration parameter tuning multi-objective optimization

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algorithm selection on Generalized Quadratic Assignment Problem Landscapes 18

Algorithm Selection on Generalized Quadratic Assignment Prob...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Beham, Andreas Wagner, Stefan Affenzeller, Michael FH Upper Austria Res Grp HEAL Hagenberg Austria Johannes Kepler Univ Linz Inst Formal Models & Verificat Linz Austria

ISBN: (纸本)9781450356183

algorithm selection is useful in decision situations where among many alternative algorithm instances one has to be chosen. This is often the case in heuristic optimization and is detailed by the well-known no-free-lunch (NFL) theorem. A consequence of the NFL is that a heuristic algorithm may only gain a performance improvement in a subset of the problems. With the present study we aim to identify correlations between observed differences in performance and problem characteristics obtained from statistical analysis of the problem instance and from fitness landscape analysis (FLA). Finally, we evaluate the performance of a recommendation algorithm that uses this information to make an informed choice for a certain algorithm instance.

关键词： fitness landscapes algorithm selection assignment problems

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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 as Superset Learning: Constructing algorithm Selectors from Imprecise Performance Data 1

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25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

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

ISBN: (数字)9783030757625

ISBN: (纸本)9783030757625;9783030757618

algorithm selection refers to the task of automatically selecting the most suitable algorithm for solving an instance of a computational problem from a set of candidate algorithms. Here, suitability is typically measured in terms of the algorithms' runtimes. To allow the selection of algorithms on new problem instances, machine learning models are trained on previously observed performance data and then used to predict the algorithms' performances. Due to the computational effort, the execution of such algorithms is often prematurely terminated, which leads to right-censored observations representing a lower bound on the actual runtime. While simply neglecting these censored samples leads to overly optimistic models, imputing them with precise though hypothetical values, such as the commonly used penalized average runtime, is a rather arbitrary and biased approach. In this paper, we propose a simple regression method based on so-called superset learning, in which right-censored runtime data are explicitly incorporated in terms of interval-valued observations, offering an intuitive and efficient approach to handling censored data. Benchmarking on publicly available algorithm performance data, we demonstrate that it outperforms the aforementioned naive ways of dealing with censored samples and is competitive to established methods for censored regression in the field of algorithm selection.

关键词： algorithm selection Superset learning Censored data

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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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Learning dataset representation for automatic machine learning algorithm selection

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KNOWLEDGE AND INFORMATION SYSTEMS 2022年第10期64卷 2599-2635页

作者： Cohen-Shapira, Noy Rokach, Lior Ben Gurion Univ Negev Software & Informat Syst Engn Dept Beer Sheva Israel Ben Gurion Univ Negev Data Sci Beer Sheva Israel Ben Gurion Univ Negev Dept Software & Informat Syst Engn SISE Beer Sheva Israel

The algorithm selection problem is defined as identifying the best-performing machine learning (ML) algorithm for a given combination of dataset, task, and evaluation measure. The human expertise required to evaluate the increasing number of ML algorithms available has resulted in the need to automate the algorithm selection task. Various approaches have emerged to handle the automatic algorithm selection challenge, including meta-learning. Meta-learning is a popular approach that leverages accumulated experience for future learning and typically involves dataset characterization. Existing meta-learning methods often represent a dataset using predefined features and thus cannot be generalized across different ML tasks, or alternatively, learn a dataset's representation in a supervised manner and therefore are unable to deal with unsupervised tasks. In this study, we propose a novel learning-based task-agnostic method for producing dataset representations. Then, we introduce TRIO, a meta-learning approach, that utilizes the proposed dataset representations to accurately recommend top-performing algorithms for previously unseen datasets. TRIO first learns graphical representations for the datasets, using four tools to learn the latent interactions among dataset instances and then utilizes a graph convolutional neural network technique to extract embedding representations from the graphs obtained. We extensively evaluate the effectiveness of our approach on 337 datasets and 195 ML algorithms, demonstrating that TRIO significantly outperforms 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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