Effectiveness of Random Search in SVM hyper-parameter tuning

Mantovani, Rafael Gomes; Rossi, André Luis Debiaso; Vanschoren, Joaquin; Bischl, Bernd; Carvalho, André C. P. L. F. de

doi:10.1109/ijcnn.2015.7280664

Cited by 104 publications

(56 citation statements)

References 21 publications

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“…The hyperparameters of LSTM, CNN-SLTM and the proposed SWT-LSTM models are optimized and listed in Table 1. The C and γ values for the SVR model are automatically optimized using grid search algorithm [27]. The forecasting results comparison is performed using three well-known error metrics that are rootmean-square error (RMSE), mean absolute percentage error (MAPE) [33] and mean bias error (MBE) with different step lengths.…”

Section: Tablementioning

confidence: 99%

A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

Yan

et al. 2019

IEEE Access

174

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Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the 'UK-DALEąŕ project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics. INDEX TERMS Energy consumption, forecasting, long short term memory, wavelet transform.

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Section: Tablementioning

confidence: 99%

A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

Yan

et al. 2019

IEEE Access

174

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“…Rafael G. Mantovani et al [9] made an investigation on random search and grid search methods. They aimed to tune the hyperparameters of the classifier called Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction

G¹,

Sumathi²

2020

IJACSA

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Text classification is a common task in machine learning. One of the supervised classification algorithm called Random Forest has been generally used for this task. There is a group of parameters in Random Forest classifier which need to be tuned. If proper tuning is performed on these hyperparameters, the classifier will give a better result. This paper proposes a hybrid approach of Random Forest classifier and Grid Search method for customer feedback data analysis. The tuning approach of Grid Search is applied for tuning the hyperparameters of Random Forest classifier. The Random Forest classifier is used for customer feedback data analysis and then the result is compared with the results which get after applying Grid Search method. The proposed approach provided a promising result in customer feedback data analysis. The experiments in this work show that the accuracy of the proposed model to predict the sentiment on customer feedback data is greater than the performance accuracy obtained by the model without applying parameter tuning.

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“…However, as this method searches all possible combinations of hyperparameters, it requires large computational time and memory [36]. RS is to find the optimal combination of hyperparameters by randomly choosing possible values of hyperparameters [37]. RS is more efficient than GS in seeking hyperparameters as only part of the possible solutions is tried.…”

Section: Implementation Of Optimization In Svr Modelsmentioning

confidence: 99%

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

Hu²,

Liu

2020

SN Appl. Sci.

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Effectiveness of Random Search in SVM hyper-parameter tuning

Abstract: mental results show that the predictive performance of models using Random Search is equivalent to those obtained using meta heuristics and Grid Search, but with a lower computational cost.

Cited by 104 publications

References 21 publications

A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households

Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction

Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization

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