An improved grid search algorithm of SVR parameters optimization

Huang, Qiujun; Mao, Jingli; Liu, Yong

doi:10.1109/icct.2012.6511415

Cited by 40 publications

(17 citation statements)

References 6 publications

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“…In our approach, a grid search with fivefold cross-validation is employed for tuning parameters, iterating over many possible parameter combinations to maximize the classification accuracy (ACC). Grid search is one of the most widely used techniques and allows us to have a transparent parameter selection (Huang et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

A t-SNE Based Classification Approach to Compositional Microbiome Data

Xie

Yang

et al. 2020

Front. Genet.

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As a data-driven dimensionality reduction and visualization tool, t-distributed stochastic neighborhood embedding (t-SNE) has been successfully applied to a variety of fields. In recent years, it has also received increasing attention for classification and regression analysis. This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE. The Aitchison distance was employed to modify the conditional probabilities in t-SNE to account for the compositionality of microbiome data. To classify a new sample, its low-dimensional features were obtained as the weighted mean vector of its nearest neighbors in the training set. Using the low-dimensional features as input, three commonly used machine learning algorithms, logistic regression (LR), support vector machine (SVM), and decision tree (DT) were considered for classification tasks in this study. The proposed approach was applied to two disease-associated microbiome datasets, achieving better classification performance compared with the classifiers built in the original high-dimensional space. The analytic results also showed that t-SNE with Aitchison distance led to improvement of classification accuracy in both datasets. In conclusion, we have developed a t-SNE based classification approach that is suitable for compositional microbiome data and may also serve as a baseline for more complex classification models.

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Section: Discussionmentioning

confidence: 99%

A t-SNE Based Classification Approach to Compositional Microbiome Data

Xie

Yang

et al. 2020

Front. Genet.

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“…To determine the optimal hyperparameters for a machine learning algorithm, one commonly used method is grid search, which involves training the model with different combinations of hyperparameters and selecting the combination that results in the best performance on a validation dataset [42][43][44][45]. Another method is random search, which involves randomly sampling hyperparameter combinations and selecting the combination that results in the best performance on a validation dataset.…”

Section: 4grid Search Methodsmentioning

confidence: 99%

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

Yang

Por

Khan

et al. 2023

Preprint

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Lung cancer detection using machine learning involves training a model on a dataset of medical images, such as CT scans, to identify patterns and features associated with lung cancer. Past researchers developed different computer aided diagnostic (CAD) systems for early prediction of lung cancer. The researchers extracted single features such as texture, morphology etc.; however, by combining the features, accuracy can be improved. In this study, we extracted Gray-level co-occurrence (GLCM), autoencoder and Haralick texture features. We combined these features and computed the performance using robust machine algorithms including Decision tree (DT), Naïve Bayes (NB) and support vector machine (SVM) with different kernel functions. The performance was evaluated using standard performance measures. The hybrid methods such as GLCM + Autoencoder, and Haralick + Autoencoder yielded highest detection performance using SVM Gaussian and radial base function (RBF) with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) with accuracy of 100% and AUC 1.00 followed by SVM polynomial yielded an accuracy of 99.89% and AUC of 1.00; GLCM + Haralick using SVM Gaussian yielded accuracy (99.56%), SVM RBF yielded accuracy (99.35%). The results reveal that the proposed feature extraction methodology can be usefully used to predict the lung cancer for further diagnosis at early stage. MSC: Artificial Intelligence, Machine Learning, Lung Cancer, cross validation

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“…Grid search is an automatic hyperparameter optimization algorithm, which is widely used in hyperparameter optimization for deep learning [55][56][57]. In grid search algorithm, each hyperparameter is first discretized to generate a discrete search space.…”

Section: Research On Models' Hyperparameter Optimizationmentioning

confidence: 99%

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

Liu,

Wu,

Shan

et al. 2024

Preprint

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Background: Accurately predicting carbon dioxide (CO2) emissions is crucial for environmental protection. Currently, there are two main issues with predicting CO2 emissions: (1) existing CO2 emission prediction models mainly rely on such as Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU), which can only model unidirectional temporal features, resulting in insufficient accuracy: (2) existing research on CO2 emissions mainly focuses on designing predictive models, without paying attention to model optimization, resulting in models being unable to achieve their optimal performance. Methods: To address the aforementioned issues, this paper proposed a framework for predicting CO2 emissions, named as CGAOA-AttBiGRU. In this framework, Attentional-Bidirectional Gate Recurrent Unit (AttBiGRU) is a prediction model that uses BiGRU units to extract bidirectional temporal features from the data, and adopts an attention mechanism to adaptively weight the bidirectional temporal features, thereby improving prediction accuracy. CGAOA is an improved Arithmetic Optimization Algorithm (AOA) used to optimize the 5 key hyperparameters of the AttBiGRU. Results: We first validated the optimization performance of the improved CGAOA algorithm on 24 benchmark functions. Then, CGAOA was used to optimize AttBiGRU and compared with 12 optimization algorithms. The results indicate that the AttBiGRU optimized by CGAOA has the best predictive performance. Finally, the optimized AttBiGRU was compared with the BiGRU, LSTM, GRU, ANN, and SVM on predicting CO2 emissions from 4 sectors in China. The experimental results showed that AttBiGRU is significantly better than the comparison models. Conclusions: This paper proposed a novel framework, CGAOA-AttBiGRU, for predicting CO2 emissions. This framework not only includes predictive models, but also optimization method for the models. This study will guide researchers to not only focus on proposing new predictive models, but also on model optimization.

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An improved grid search algorithm of SVR parameters optimization

Cited by 40 publications

References 6 publications

A t-SNE Based Classification Approach to Compositional Microbiome Data

A t-SNE Based Classification Approach to Compositional Microbiome Data

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

CGAOA-AttBiGRU: A novel deep learning framework for forecasting CO 2 emissions

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