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Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms

JOURNAL OF BIG DATA 2023年第1期10卷 14页

作者： Garouani, Moncef Ahmad, Adeel Bouneffa, Mourad Hamlich, Mohamed Univ Littoral Cote dOpale LISIC Lab Informat Signal & Image Cote Opale UR 4491 F-62100 Calais E France Univ Hassan 2 CCPS Lab ENSAM Casablanca Morocco HESTIM Study & Res Ctr Engn & Management CERIM Casablanca Morocco

The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new Autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.

关键词： algorithm selection AutoML Meta-learning Meta-features Data representation kNN Autoencoder

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AutoFe-Sel: A Meta-learning based methodology for Recommending Feature Subset selection algorithms

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2023年第7期17卷 1773-1793页

作者： Khan, Irfan Zhang, Xianchao Ayyasamy, Ramesh Kumar Ali, Rahman Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Univ Tunku Abdul Rahman Fac Informat & Commun Technol Kampar 31900 Malaysia Univ Peshawar QACC Peshawar 25120 Pakistan

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the meta modeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

关键词： algorithm selection Feature selection Recommender systems Meta-learning

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GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver selection 24

GraSS: Combining Graph Neural Networks with Expert Knowledge...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Zhang, Zhanguang Chetelat, Didier Cotnareanu, Joseph Ghose, Amur Xiao, Wenyi Zhen, Hui-Ling Zhang, Yingxue Hao, Jianye Coates, Mark Yuan, Mingxuan Huawei Noahs Ark Lab Montreal PQ Canada McGill Univ Montreal PQ Canada Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9798400704901

Boolean satisfiability (SAT) problems are routinely solved by SAT solvers in real-life applications, yet solving time can vary drastically between solvers for the same instance. This has motivated research into machine learning models that can predict, for a given SAT instance, which solver to select among several options. Existing SAT solver selection methods all rely on some hand-picked instance features, which are costly to compute and ignore the structural information in SAT graphs. In this paper we present GraSS, a novel approach for automatic SAT solver selection based on tripartite graph representations of instances and a heterogeneous graph neural network (GNN) model. While GNNs have been previously adopted in other SAT-related tasks, they do not incorporate any domain-specific knowledge and ignore the runtime variation introduced by different clause orders. We enrich the graph representation with domain-specific decisions, such as novel node feature design, positional encodings for clauses in the graph, a GNN architecture tailored to our tripartite graphs and a runtime-sensitive loss function. Through extensive experiments, we demonstrate that this combination of raw representations and domain-specific choices leads to improvements in runtime for a pool of seven state-of-the-art solvers on both an industrial circuit design benchmark, and on instances from the 20-year Anniversary Track of the 2022 SAT Competition.

关键词： GNN SAT algorithm selection

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How to Select algorithms for Predictive Maintenance: An Economic Decision Model and Real-World Instantiation

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年第2期24卷 361-394页

作者： Fabri, Lukas Haeckel, Bjoern Keller, Robert Oberlaender, Anna Maria Univ Appl Sci Augsburg Fac Comp Sci FIM Res Ctr Augsburg Germany Univ Augsburg Project Grp Business & Informat Syst Engn Fraunhof Augsburg Germany Univ Appl Sci Kempten Fac Tourism Management FIM Res Ctr Kempten Germany Univ Bayreuth FIM Res Ctr Bayreuth Germany

Predictive maintenance represents a promising application of Artificial Intelligence in the industrial context. The evaluation and selection of predictive maintenance algorithms primarily rely on statistical measures such as absolute and relative prediction errors. However, a purely statistical approach to algorithm selection may not necessarily lead to the optimal economic outcome, as the two types of prediction errors are negatively correlated, thus, cannot be jointly optimized, and are associated with different costs. As the current literature lacks corresponding guidance, we developed a decision model for industrial full-service providers, applying an economic perspective to selecting predictive maintenance algorithms. The decision model was instantiated and evaluated in a real-world setting with a European machinery company providing full-service solutions in the field of car wash systems. Building on sensor data from 4.9 million car wash cycles, the instantiation demonstrates the applicability and effectiveness of the decision model with fidelity to a real-world phenomenon. In sum, the decision model provides economic insights into the trade-off between the algorithms' error types and enables users to focus on economic concerns in algorithm selection. Our work contributes to the prescriptive knowledge of algorithm selection and predictive maintenance in line with the consideration of different types of cost.

关键词： algorithm selection decision model economic perspective machine learning prediction error predictive maintenance

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selection of image classifiers for noisy images through metalearning 7

Selection of image classifiers for noisy images through meta...

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7th International Conference on Machine Vision and Applications (ICMVA)

作者： de Hoog, Jens Anwar, Ali Hellinckx, Peter Mercelis, Siegfried Univ Antwerp IMEC IDLab Fac Appl Engn B-2000 Antwerp Belgium Univ Antwerp M4S Fac Appl Engn B-2020 Antwerp Belgium

ISBN: (纸本)9798400716553

Machine Learning (ML) models are being deployed in numerous applications, ranging from autonomous vehicles to robotics. However, the performance of these ML models is highly dependent on situations within the application, such as noisy inputs or distorted images. Dealing with these different situations is complex, as the 'no free lunch' theorem states there is no single ML model that is able to achieve high performances in all situations. Selecting the model that is most suitable for a given context would therefore be beneficial. Although metalearning-based algorithm selection (AS) has proven to be an effective way of selecting the most suitable ML model for complete datasets, limited research has been done regarding dealing with dynamic data samples. This is especially important for noisy data samples, as noise highly affects the performance of ML models, such as image classifiers. Employing metalearning-based ML for selecting the best image classifier to classify the given noisy images has not been done before. Therefore, this work presents a methodology for carrying out this selection through metalearning. Based on the learned relationship between classification performance and the context of the noisy image, our AS strategy is able to select the most optimal image classifier for a specific situation. We compare different metamodels that carry out this selection, while we also compare the use of regression-based and classification-based selection. In our results, we show that there is a clear benefit in exploiting the robustness of different classifiers on the CIFAR-100 dataset via AS, as the per-class and overall classification accuracies are increased. We also compare our metalearing-based selection with random selection of image classifiers to show the significance of our AS methodology. Hence, this research enables future works to select the most suitable image classifying AS given the image context and noise distribution parameters.

关键词： algorithm selection metalearning image classification neural networks

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MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data

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NEUROCOMPUTING 2014年第1期127卷 52-64页

作者： Debiaso Rossi, Andre Luis de Leon Ferreira de Carvalho, Andre Carlos Ponce Soares, Carlos de Souza, Bruno Feres Univ Sao Paulo Inst Ciencias Matemat & Computac Sao Carlos SP Brazil Univ Porto Fac Engn INESC TEC P-4100 Oporto Portugal

Dynamic real-world applications that generate data continuously have introduced new challenges for the machine learning community, since the concepts to be learned are likely to change over time. In such scenarios, an appropriate model at a time point may rapidly become obsolete, requiring updating or replacement. As there are several learning algorithms available, choosing one whose bias suits the current data best is not a trivial task. In this paper, we present a meta-learning based method for periodic algorithm selection in time-changing environments, named MetaStream. It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination. Experimental results for two real regression problems showed that MetaStream is able to improve the general performance of the learning system compared to a baseline method and an ensemble-based approach. (C) 2013 Elsevier B.V. All rights reserved.

关键词： algorithm selection Meta-learning Data streams

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On the Instance Dependence of Parameter Initialization for the Quantum Approximate Optimization algorithm: Insights via Instance Space Analysis

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INFORMS JOURNAL ON COMPUTING 2025年第1期37卷 1-196, C2页

作者： Katial, Vivek Smith-Miles, Kate Hill, Charles Hollenberg, Lloyd Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia Univ Melbourne Sch Phys Parkville Vic 3010 Australia Univ New South Wales Sch Phys Sydney NSW 2052 Australia

The quantum approximate optimization algorithm (QAOA) tackles combinatorial optimization problems in a quantum computing context, where achieving globally optimal and exact solutions is not always feasible because of classical computational constraints or problem complexity. The performance of QAOA generally depends on finding sufficiently good parameters that facilitate competitive approximate solutions. However, this is fraught with challenges, such as "barren plateaus," making the search for effective parameters a nontrivial endeavor. More recently, the question of whether such an optimal parameter search is even necessary has been posed, with some studies showing that optimal parameters tend to be concentrated on certain values for specific types of problem instances. However, these existing studies have only examined specific instance classes of Maximum Cut, so it is uncertain if the claims of instance independence apply to a diverse range of instances. In this paper, we use instance space analysis to study QAOA parameter initialization strategies for the first time, providing a comprehensive study of how instance characteristics affect the performance of initialization strategies across a diverse set of graph types and weight distributions. Unlike previous studies that focused on specific graph classes (e.g., d-regular or Erdos-Renyi), our work examines a much broader range of instance types, revealing insights about parameter transfer between different graph classes. We introduce and evaluate a new initialization strategy, quantum instance-based parameter initialization, that leverages instance-specific information, demonstrating its effectiveness across various instance types. Our analysis at higher QAOA depths (p = 15) provides insights into the effectiveness of different initialization strategies beyond the low-depth circuits typically studied.

关键词： algorithm selection instance space quantum computing optimization MaxCut

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A cross-benchmark examination of feature-based algorithm selector generalization in single-objective numerical optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Cenikj, Gjorgjina Petelin, Gasper Eftimov, Tome Jozef Stefan Inst Comp Syst Dept Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia Jamova Cesta 39 Ljubljana 1000 Slovenia

The task of selecting the best optimization algorithm for a particular problem is known as algorithm selection (AS). This involves training a model using landscape characteristics to predict algorithm performance, but a key challenge remains: making AS models generalize effectively to new, untrained benchmark suites. This study assesses AS models' generalizability in single -objective numerical optimization across diverse benchmark suites. Using Exploratory Landscape Analysis (ELA) and transformer -based (TransOpt) features, the research investigates their individual and combined effectiveness in AS across four benchmarks: BBOB, AFFINE, RANDOM, and ZIGZAG. AS models perform differently based on benchmark suite similarities in algorithm performance distributions and single -best solvers. When suites align, these models underperform against a baseline predicting mean algorithm performance;yet, they outperform the baseline when suites differ in performance distributions and solvers. The AS models trained using the ELA landscape features are better than the models trained using the TransOpt features on the BBOB and AFFINE benchmark suites, while the opposite is true for the RANDOM benchmark suite. Ultimately, the study reveals challenges in accurately capturing algorithm performance through problem landscape features (ELA or TransOpt), impacting AS model applicability.

关键词： algorithm selection Multi-target regression Generalization Benchmarking

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Selecting, optimizing and externally validating a preexisting machine-learning regression algorithm for estimating waist circumference

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 169卷 107909-107909页

作者： Phillips-Farfan, Bryan V. Inst Nacl Pediat Lab Nutr Expt Alcaldia Coyoacan Insurgentes Sur 3700Letra C Mexico City 04530 Mexico

Obesity, typically defined by the body mass index (BMI), has well known negative health effects. However, the BMI has serious deficiencies in predicting the adverse risks associated to obesity. Waist circumference (WC) is an alternative to define obesity and a better disease predictor according to the literature. However, old databases often lack this information, it is inaccurate (collected via self-report) or it is incomplete. Thus, this study accurately assesses WC using machine learning. The novel approaches are: 1) predictor variables (weight, height, age and sex) likely to appear in most data sets are used. 2) Publicly available data (including non-adults) and algorithms are used. 3) Systematic methods for data cleanup, model selection, hyperparameter optimization and external validation are performed. Data are cleaned: one variable per column, no special codes, missing values or outliers. Preexisting regression algorithms are gaged by cross-validation, using one data set. The hyperparameters of the best performing algorithm are optimized. The tuned algorithm is externally validated with other data sets by cross-validation. In spite of the limited number of features, the tuned algorithm outperforms prior WC approximations, using the same or similar predictor variables. The tuned algorithm enables using data where WC is not measured, is incomplete or is unreliable. A similar approach would be useful to estimate other variables of interest.

关键词： Machine-learning algorithm selection Hyperparameter optimization Model inspection External cross-validation Regression

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3D meta-classification: A meta-learning approach for selecting 3D point-cloud classification algorithm

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INFORMATION SCIENCES 2024年 662卷

作者： Xu, Fan Chen, Jun Shi, Yizhou Ruan, Tianchen Wu, Qihui Zhang, Xiaofei Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 211106 Peoples R China

algorithm selection technology, aimed at choosing the most suitable algorithm for a given machine learning task, has achieved significant success in the domain of 2D vision. However, few studies have explored its application to the 3D point cloud domain. The rapid proliferation and development of 3D point cloud classification algorithms underscore the urgent need for exploration in selecting these algorithms. In this paper, we propose a novel meta -learningbased 3D classification approach, termed 3D meta -classification, to address this gap. The approach operates at both the base -level and meta -level phases. At the base level, candidate 3D classification algorithms constantly classify various 3D datasets, recording their classification performance as empirical knowledge. As for the meta -level phase, it specifically tailors the 3D meta -knowledge generator, 3D meta -feature extractor, and 3D meta -database constructor to establish the 3D meta -database, capturing the relationship between the 3D meta -features and empirical knowledge. Leveraging the 3D meta -database, the 3D classification meta -learner trains the meta -model and predicts suitable algorithms for new incoming 3D datasets. Extensive experiments are conducted to select the best -performing classification algorithm for specific 3D datasets from ModelNet40. The results demonstrate the effectiveness of the proposed 3D metaclassification model;the accuracies of one -from -two and one -from -three algorithm selection tasks reach over 98% and 80%, respectively.

关键词： Meta-learning 3D point-cloud classification algorithm selection 3D meta-features 3D classification meta-learner

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