Towards the Automation of Industrial Data Science: A Meta-learning based Approach

Garouani, Moncef; Ahmad, Adeel; Bouneffa, Mourad; Lewandowski, Arnaud; Bourguin, Grégory; Hamlich, Mohamed

doi:10.5220/0010457107090716

Cited by 14 publications

(5 citation statements)

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“…We opted to use the k-nearest neighbor meta-model for comparison with the decision tree meta-model, as the kNN classifier is one of the most commonly employed algorithms for obtaining top-k rankings in meta-learning, Garouani et al (2021b). We used the Euclidean Distance metric and forecasted the optimal pipeline configuration by taking a weighted average of each individual neighbor's ranking in order to determine the closest neighbors of the dataset.…”

Section: Resultsmentioning

confidence: 99%

“…Many studies have demonstrated the effectiveness of meta-learning when addressing the Algorithms Selection and Parameterization. In this context, the applications of AutoML with the use of Meta-Learning (MtL) have the potential to facilitate the efficiency of machine learning solutions Garouani et al (2021a). This can help to alleviate the repetitive, time-consuming, and resource-intensive tasks that data scientists and practitioners may encounter.…”

Section: Introductionmentioning

confidence: 99%

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An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

Garouani,

Ahmad,

Bouneffa

2024

Preprint

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Considerable progress has been made in the recent literature studies to tackle the Algorithms Selection and Parametrization (ASP) problem, which is diversified in multiple meta-learning setups. Yet there is a lack of surveys and comparative evaluations that critically analyze, summarize and assess the performance of existing methods. In this paper, we provide an overview of the state of the art in this continuously evolving field. The survey sheds light on the motivational reasons for pursuing classifiers selection through meta-learning. In this regard, Automated Machine Learning (AutoML) is usually treated as an ASP problem under the umbrella of the democratization of machine learning. Accordingly, AutoML makes machine learning techniques accessible to domain scientists who are interested in applying advanced analytics but lack the required expertise. It can ease the task of manually selecting ML algorithms and tuning related hyperparame-ters. We comprehensively discuss the different phases of classifiers selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we propose a benchmark knowledge base of 4 millions previously learned models and present extensive comparative evaluations of the prominent methods for classifiers selection based on 08 classification algorithms and 400 benchmark datasets. The comparative study quantitatively assesses the performance of algorithms selection methods along while emphasizing the strengths and limitations of existing studies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

Garouani,

Ahmad,

Bouneffa

2024

Preprint

Self Cite

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show abstract

“…Some approaches attempt to automatically and simultaneously choose a learning algorithm and optimize its hyper-parameters. These approaches are also known as Combined Algorithm Selection and Hyper-parameters optimization problem (CASH) [7][8][9][25][26][27][28]. Table 2 shows a comparison among some of the most popular AutoML tools, in terms of training framework, supported ML tasks, automatic features engineering, user interface and process transparency.…”

Section: Automated Machine Learningmentioning

confidence: 99%

“…End users, by nature, may wonder about the reasoning behind how and why algorithms make or arrive to decisions [29]. As the complexity of the AI algorithms and systems grows, they are viewed as "black-boxes" [27,32]. Increasing complexity can result in the lack of transparency that hampers understanding the reasoning of these systems, which negatively affects the users trustiness.…”

Section: Explainable Aimentioning

confidence: 99%

Towards big industrial data mining through explainable automated machine learning

Garouani

Ahmad

Bouneffa

et al. 2022

Int J Adv Manuf Technol

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Section: Automated Machine Learningmentioning

confidence: 99%

Towards Big Industrial Data Mining Through Explainable Automated Machine Learning

Garouani

Ahmad

Bouneffa

et al. 2021

Preprint

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Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence(AI) models but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameter configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field.

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Towards the Automation of Industrial Data Science: A Meta-learning based Approach

Cited by 14 publications

References 0 publications

An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization

Towards big industrial data mining through explainable automated machine learning

Towards Big Industrial Data Mining Through Explainable Automated Machine Learning

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