Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation

Soper, Daniel S.

doi:10.3390/electronics10161973

Cited by 33 publications

(18 citation statements)

References 26 publications

(35 reference statements)

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“…The Stochastic Gradient Descent (SGD) has been used to train the parameters of all the proposed models and a greedy k-fold cross validation approach [31] has been used to estimate the hyper-parameters of the optimiser (see Table I).…”

Section: Resultsmentioning

confidence: 99%

Drone Footage Wind Turbine Surface Damage Detection

Foster

Best

Gianni

et al. 2022

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

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In this work a new publicly available dataset of wind turbine surface damage images is presented. Moreover, a comparison between ResNet-101 Faster R-CNN and YOLOv5 for Wind Turbine Surface Damage Detection is analysed and performance of these models on drone footage with active turbines is also discussed. Results show that YOLOv5 outperforms ResNet-101 Faster R-CNN in predicting the bounding box coordinates of the damaged surfaces of the wind turbines. However, unlike YOLOv5, ResNet-101 Faster R-CNN estimates an entire instance of damage as a single prediction.

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Section: Resultsmentioning

confidence: 99%

Drone Footage Wind Turbine Surface Damage Detection

Foster

Best

Gianni

et al. 2022

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

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“…During iteration, each fold is used to train the k − 1 candidate model and the performance of the model is measured with the remaining fold. is process is repeated until each fold is fully used as a validation set, and a total of k retraining and validation processes are performed for each candidate model [43][44][45][46]. Figure 3 shows the k 10 fold cross-validation process used in the study.…”

Section: K-fold Cross Validationmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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“…Therefore, it is critical to obtain the hyper-parameters combination with the best generalization performance. In this study, we use the K-Fold cross-validation method (Soper, 2021) to evaluate the model performance. However, the cross-validation results of ML models are sensitive to the choice of hyper-parameters.…”

Section: Hyper-parameters Optimization For Regression Modelsmentioning

confidence: 99%

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Yan

Lei

Jiang

et al. 2022

Front. Environ. Sci.

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Accurate runoff prediction can provide a reliable decision-making basis for flood and drought disaster prevention and scientific allocation of water resources. Selecting appropriate predictors is an effective way to improve the accuracy of runoff prediction. However, the runoff process is influenced by numerous local and global hydrometeorological factors, and there is still no universal approach about the selection of suitable predictors from these factors. To address this problem, we proposed a runoff prediction model by combining machine learning (ML) and feature importance analysis (FIA-ML). Specifically, take the monthly runoff prediction of Yingluoxia, China as an example, the FIA-ML model uses mutual information (MI) and feature importance ranking method based on random forest (RF) to screen suitable predictors, from 130 global climate factors and several local hydrometeorological information, as the input of ML models, namely the hybrid kernel support vector machine (HKSVM), extreme learning machine (ELM), generalized regression neural network (GRNN), and multiple linear regression (MLR). An improved particle swarm optimization (IPSO) is used to estimate model parameters of ML. The results indicated that the performance of the FIA-ML is better than widely-used long short-term memory neural network (LSTM) and seasonal autoregressive integrated moving average (SARIMA). Particularly, the Nash-Sutcliffe Efficiency coefficients of the FIA-ML models with HKSVM and ELM were both greater than 0.9. More importantly, the FIA-ML models can explicitly explain which physical factors have significant impacts on runoff, thus strengthening the physical meaning of the runoff prediction model.

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Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation

Cited by 33 publications

References 26 publications

Drone Footage Wind Turbine Surface Damage Detection

Drone Footage Wind Turbine Surface Damage Detection

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

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