Empirical comparison of cross-validation and internal metrics for tuning SVM hyperparameters

Duarte, Edson Costa; Wainer, Jacques

doi:10.1016/j.patrec.2017.01.007

Cited by 68 publications

(34 citation statements)

References 8 publications

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“…While (semi-)parametric algorithms cannot be tuned in the same way as machine-learning algorithms (although some perform an internal optimization, e.g. the implementation of the GAM in the mgcv package from Wood (2006)), hyperparameters of machine-learning algorithms need to be tuned to achieve optimal performances (Bergstra & Bengio, 2012;Duarte & Wainer, 2017;Hutter et al, 2011). Note that for parametric models the term "parameter" is often used to refer to the regression coefficients of each predictor in the fitted model.…”

Section: Tuning Of Hyperparametersmentioning

confidence: 99%

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Schratz

Muenchow

Iturritxa

et al. 2019

Ecological Modelling

366

176

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Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly predictors.Results show that GAM and Random Forest (RF) (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24%) and 0.213 (30%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data. The models developed in this study enhance the detection of Diplodia sapinea in the Basque Country compared to previous studies.

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Section: Tuning Of Hyperparametersmentioning

confidence: 99%

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Schratz

Muenchow

Iturritxa

et al. 2019

Ecological Modelling

366

176

View full text Add to dashboard Cite

show abstract

“…To solve individual noise points, the SVM algorithm employed allows certain error classification to occur under given accuracy ε. The penalty parameter C ≥ 0 and relaxation variable ε ≥ 0 were introduced to ensure fault tolerance of the model [70].…”

Section: Employed Svm Algorithmmentioning

confidence: 99%

Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

Zhang

Peng

2020

Sustainability

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The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

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“…GridSearch is basically a model for hyper parameter optimization. Hyper parameter tuning is an important task in SVM to extract more accurate results [30]- [33]. In Grid-Search, different models having different parameter values are trained and then evaluated using cross validation.…”

Section: Classificationmentioning

confidence: 99%

SVM Optimization for Sentiment Analysis

Ahmad¹,

Aftab²,

Bashir³

et al. 2018

ijacsa

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Abstract-Exponential growth in mobile technology and mini computing devices has led to a massive increment in social media users, who are continuously posting their views and comments about certain products and services, which are in their use. These views and comments can be extremely beneficial for the companies which are interested to know about the public opinion regarding their offered products or services. This type of public opinion otherwise can be obtained via questionnaires and surveys, which is no doubt a difficult and complex task. So, the valuable information in the form of comments and posts from micro-blogging sites can be used by the companies to eliminate the flaws and to improve the products or services according to customer needs. However, extracting a general opinion out of a staggering number of users' comments manually cannot be feasible. A solution to this is to use an automatic method for sentiment mining. Support Vector Machine (SVM) is one of the widely used classification techniques for polarity detection from textual data. This study proposes a technique to tune the SVM performance by using grid search method for sentiment analysis. In this paper, three datasets are used for the experiment and performance of proposed technique is evaluated using three information retrieval metrics: precision, recall and f-measure.

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Empirical comparison of cross-validation and internal metrics for tuning SVM hyperparameters

Cited by 68 publications

References 8 publications

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data

Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

SVM Optimization for Sentiment Analysis

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