Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization

Candelieri, Antonio; Giordani, Ilaria; Archetti, Francesco; Barkalov, Konstantin; Meyerov, Iosif B.; Половинкин, А. Н.; Sysoyev, Alexander; Zolotykh, Nikolai Yu.

doi:10.1016/j.cor.2018.01.013

Cited by 72 publications

(39 citation statements)

References 16 publications

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“…According to the emerging interest on BO, to solve black-box and expensive optimization processes [3] [4], we have proposed the BO as a global optimization algorithm for the estimation of a promising starting model for FWI. We considered two alternative acquisition functions for BO and test them on a 2D acoustic FWI benchmark problem, namely the Marmousi model.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Optimization for Full Waveform Inversion

Galuzzi

Perego

Candelieri

et al. 2018

AIRO Springer Series

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Full Waveform Inversion (FWI) is a computational method to estimate the physical features of Earth subsurface from seismic data, leading to the minimization of a misfit function between the observed data and the predicted ones, computed by solving the wave equation numerically. This function is usually multimodal, and any gradient-based method would likely get trapped in a local minimum, without a suitable starting point in the basin of attraction of the global minimum. The starting point of the gradient procedure can be provided by an exploratory stage performed by an algorithm incorporating random elements. In this paper, we show that Bayesian Optimization (BO) can offer an effective way to structure this exploration phase. The computational results on a 2D acoustic FWI benchmark problem show that BO can provide a starting point in the parameter space from which the gradient-based method converges to the global optimum.

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Section: Discussionmentioning

confidence: 99%

Bayesian Optimization for Full Waveform Inversion

Galuzzi

Perego

Candelieri

et al. 2018

AIRO Springer Series

Self Cite

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“…A hyperparameter is a parameter whose esteem is utilized to control the learning procedure. A similar sort of machine-learning model can require distinctive limitations, loads or learning rates to sum up various information designs [20]. These measures are called hyperparameters and must be tuned with the goal that the model can ideally tackle the machine-learning issue.…”

Section: Hyper-tuningmentioning

confidence: 99%

“…For SVC, the optimum performance is achieved by tuning C, gamma and kernel [20]. After tuning, these parameters are found as: 'C': 1, 'gamma': 0.0001, 'kernel': 'rbf' .…”

Section: Support Vector Machinementioning

confidence: 99%

A robust voting approach for diabetes prediction using traditional machine learning techniques

Mahabub

2019

SN Appl. Sci.

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The noteworthy advances in biotechnology and biomedical sciences have prompted a huge creation of information, for example, high throughput genetic information and clinical data, produced from extensive Electronic Health Records. To this end, utilization of machine learning and data mining techniques in biosciences is by and by crucial and fundamental in endeavors to change cleverly all accessible data into profitable knowledge. Diabetes mellitus is characterized as a gathering of metabolic issue applying critical weight on human health around the world. Broad research in all parts of diabetes (determination, pathophysiology, treatment, and so forth.) has prompted the age of tremendous measures of information. The point of the present examination is to direct an orderly audit of the uses of machine-learning, data mining strategies and instruments in the field of diabetes. The main theme of this work is to provide a system which can prognosticate the diabetes in patients with better accuracy. Here, eleven well-known machine-learning algorithms like Naïve Bayes, K-NN, SVM, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, Ada Boosting etc. are used for detection of diabetes at an early stage. The evaluations of all the eleven algorithms are examined on various parameters like accuracy, precision, F-measure and recall. After cross-validation and hyper-tuning, the best three machine-learning algorithms are determined and then used in Ensemble Voting Classifier. The experimental results affirm that the pointed framework can accomplish to outstanding outcome of almost 86% accuracy of the Pima Indians Diabetes Database.

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“…Recently, Bayesian optimization (BO) (Shahriari et al 2015;Frazier 2018) is becoming one of the most widely adopted strategies for global optimization of multi-extremal, and expensive-to-evaluate objective functions related to, e.g., sensor networks (Garnett et al 2010), drug design (Meldgaard et al 2018), time-series forecasting (Candelieri et al 2018a), inversion problems (Perdikaris and Karniadakis 2016; Galuzzi et al 2018), and robotics (Olofsson et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter optimization for recommender systems through Bayesian optimization

et al. 2020

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Recommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search.

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Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization

Cited by 72 publications

References 16 publications

Bayesian Optimization for Full Waveform Inversion

Bayesian Optimization for Full Waveform Inversion

A robust voting approach for diabetes prediction using traditional machine learning techniques

Hyperparameter optimization for recommender systems through Bayesian optimization

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