A hyperparameters selection technique for support vector regression models

Tsirikoglou, Panagiotis; Abraham, Simon Michel; Contino, Francesco; Lacor, Christian; Ghorbaniasl, Ghader

doi:10.1016/j.asoc.2017.07.017

Cited by 51 publications

(27 citation statements)

References 18 publications

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“…C is the regularization parameter and it controls a tradeoff between the allowed error deviations and the complexity of the decision function, ε is related to the error penalty of the loss function and γ represents the width of the RBF kernel. Several optimization strategies have been suggested [41] and many involve hyper-parameter fine-tuning using computationally exhaustive grid-search and cross-validation approaches. However, the grid-search technique despite its high accuracy results, take a prohibitive computation time to achieve reliable results specially for schemes where re-configurations of the SVM model are required due to changes in the input calibration sets.…”

Section: Methodsmentioning

confidence: 99%

Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions

Passos

Rodrigues

Cavaco

et al. 2019

Sensors

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In this paper we report a method to determine the soluble solids content (SSC) of ‘Rocha’ pear (Pyrus communis L. cv. Rocha) based on their short-wave NIR reflectance spectra (500–1100 nm) measured in conditions similar to those found in packinghouse fruit sorting facilities. We obtained 3300 reflectance spectra from pears acquired from different lots, producers and with diverse storage times and ripening stages. The macroscopic properties of the pears, such as size, temperature and SSC were measured under controlled laboratory conditions. For the spectral analysis, we implemented a computational pipeline that incorporates multiple pre-processing techniques including a feature selection procedure, various multivariate regression models and three different validation strategies. This benchmark allowed us to find the best model/preproccesing procedure for SSC prediction from our data. From the several calibration models tested, we have found that Support Vector Machines provides the best predictions metrics with an RMSEP of around 0.82 ∘Brix and 1.09 ∘Brix for internal and external validation strategies respectively. The latter validation was implemented to assess the prediction accuracy of this calibration method under more ‘real world-like’ conditions. We also show that incorporating information about the fruit temperature and size to the calibration models improves SSC predictability. Our results indicate that the methodology presented here could be implemented in existing packinghouse facilities for single fruit SSC characterization.

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Section: Methodsmentioning

confidence: 99%

Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions

Passos

Rodrigues

Cavaco

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…These parameters are called hyperparameters, such as penalized parameter, C ; ε‐insensitive loss function, ε ; and the kernel parameter. The SVR performance is very sensitive to the selection of these hyperparameters, and there is no mathematical‐based procedure for deriving the exact desired values 59 . As a result, the selection of those hyperparameters is a crucial part of the research on SVR.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

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Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection. Nature‐inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, the opposition‐based learning Harris hawks optimization algorithm (HHOA‐OBL) is proposed to optimize the hyperparameters of the v‐SVR with embedding the feature selection simultaneously. The experimental results over four datasets show that the HHOA‐OBL outperforms the standard Harris hawks optimization algorithm, grid search, and cross‐validation methods, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the HHOA‐OBL confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared with other nature‐inspired algorithms, which proves the ability of HHOA‐OBL in searching for the best hyperparameters values and selecting the most informative features for prediction tasks. Thus, the experiments and comparisons support the performance of the proposed approach in making predictions in other real applications.

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“…It probably was caused by a relatively poor accuracy of the olfactory measured results. Because the SVR algorithm is very sensitive to the noise in the training data [35]. Therefore, the noise (arising from the error of olfactory evaluation) in the training samples can easily affect the fitting effect of the SVR model.…”

Section: Odor Intensity Predictive Performance Of the Svr Modelmentioning

confidence: 99%

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

Yan

Liu

2020

Sensors

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The complex odor interaction between odorants makes it difficult to predict the odor intensity of their mixtures. The analysis method is currently one of the factors limiting our understanding of the odor interaction laws. We used a support vector regression algorithm to establish odor intensity prediction models for binary esters, aldehydes, and aromatic hydrocarbon mixtures, respectively. The prediction accuracy to both training samples and test samples demonstrated the high prediction capacity of the support vector regression model. Then the optimized model was used to generate extra odor data by predicting the odor intensity of more simulated samples with various mixing ratios and concentration levels. Based on these olfactory measured and model predicted data, the odor interaction was analyzed in the form of contour maps. This intuitive method showed more details about the odor interaction pattern in the binary mixture. We found that that the antagonism effect was commonly observed in these binary mixtures and the interaction degree was more intense when the components' mixing ratio was close. Meanwhile, the odor intensity level of the odor mixture barely influenced the interaction degree. The machine learning algorithms were considered promising tools in odor researches.3 of 12 standard in odor intensity evaluation [26]. More details about the testing procedure and environmental requirements were described in these references. In this study, the collected dataset contains 31 samples of binary mixture EA+BA, 21 samples of EA+EB, 22 samples of BA+EB, 24 samples of PA+VA, 24 samples of PA+HEP, 24 samples of VA+HEP, 34 samples of B+T, 31 samples of B+E, and 24 samples of T+E.

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A hyperparameters selection technique for support vector regression models

Cited by 51 publications

References 18 publications

Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions

Non-Destructive Soluble Solids Content Determination for ‘Rocha’ Pear Based on VIS-SWNIR Spectroscopy under ‘Real World’ Sorting Facility Conditions

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

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