Hyper-Optimization tools comparison for parameter tuning applications

Maurice, Camille; Madrigal, Francisco; Lerasle, Frédéric

doi:10.1109/avss.2017.8078499

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Thanks to the efforts of recent studies, today's database practitioners are well-equipped with many techniques of configuration tuning. However, when we look from a broader view, we can find plenty of toolkits and algorithms designed for the blackbox optimization, especially hyper-parameter optimization (HPO) approaches [6,30,49,60,82,89]. HPO aims to find the optimal hyper-parameter configurations of a machine learning algorithm as rapidly as possible to minimize the corresponding loss function.…”

Section: Motivationmentioning

confidence: 99%

Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

Zhang¹,

Chang²,

Yang³

et al. 2021

Preprint

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Recently, using automatic configuration tuning to improve the performance of modern database management systems (DBMSs) has attracted increasing interest from the database community. This is embodied with a number of systems featuring advanced tuning capabilities being developed. However, it remains a challenge to select the best solution for database configuration tuning, considering the large body of algorithm choices. In addition, beyond the applications on database systems, we could find more potential algorithms designed for configuration tuning. To this end, this paper provides a comprehensive evaluation of configuration tuning techniques from a broader perspective, hoping to better benefit the database community. In particular, we summarize three key modules of database configuration tuning systems and conduct extensive ablation studies using various challenging cases. Our evaluation demonstrates that the hyper-parameter optimization algorithms can be borrowed to further enhance the database configuration tuning. Moreover, we identify the best algorithm choices for different modules. Beyond the comprehensive evaluations, we offer an efficient and unified database configuration tuning benchmark via surrogates that reduces the evaluation cost to a minimum, allowing for extensive runs and analysis of new techniques.

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Section: Motivationmentioning

confidence: 99%

Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

Zhang¹,

Chang²,

Yang³

et al. 2021

Preprint

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“…Instead, we use a hyper-parameter optimization method: SMAC [7], which has up to 900 citations. It has proven already to increase the performances of computer vision algorithms by tuning hyper-parameters [4,19]. In order to find the set of parameters values, it requires a measure of the algorithm's performance.…”

Section: Methodsmentioning

confidence: 99%

A New Bayesian Modeling for 3D Human-Object Action Recognition

Maurice

Madrigal

Monin

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…For hyper parameter optimization, the paper [19] compared TPE (Tree-structured Parzen Estimator), MCMC (Markov chain Monte Carlo) and SMAC (Sequential Modelbased Algorithm Configuration) optimisation techniques and found TPE to be around 4% more accurate than the other optimisers. There are few papers comparing different hyper parameter optimisation algorithms.…”

Section: Max Flames = Round (N-i*(n-1)/t) (1)mentioning

confidence: 99%

Phishing Web Page Detection Using Optimised Machine Learning

Stobbs

Issac

Jacob

2020

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Phishing is a type of social engineering attack that can affect any company or anyone. This paper explores the effect that different features and optimisation techniques have on the accuracy of intelligent phishing detection using machine learning algorithms. This paper explores both hyperparameter optimisation as well as feature selection optimisation. For hyperparameter tuning, both TPE (Tree-structured Parzen Estimator) and GA (Genetic Algorithm) were tested, with the best option being model dependent. For feature selection, GA, MFO (Moth Flame Optimisation) and PSO (Particle Swarm Optimisation) were used with PSO working best with a Random Forest model. This work used URL (Uniform Resource Locator), DOM (Document Object Model) structure, page rank and page information related features. This research found that the best combination was Random Forest using PSO for feature selection and TPE for hyperparameter optimisation, giving an accuracy of 99.33%.

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Hyper-Optimization tools comparison for parameter tuning applications

Cited by 10 publications

References 11 publications

Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

A New Bayesian Modeling for 3D Human-Object Action Recognition

Phishing Web Page Detection Using Optimised Machine Learning

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