Feature Selection Method Based on Mutual Information and Support Vector Machine

Liu, Gang; Yang, Chunlei; Liu, Sen; Xiao, Chun-Bao; Song, Bin

doi:10.1142/s021800142150021x

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…The input variable selection is accomplished through normalized mutual information (NMI). NMI is adopted as the similarity measure, as equation ( 1), which measures how much information, on average, one random variable provides to another (Danon et al, 2006;Liu et al, 2021a;Zhou et al, 2021a). The advantage of NMI is that it can measure the nonlinear relationship between variables.…”

Section: Normalized Mutual Informationmentioning

confidence: 99%

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

Cui,

Qian,

et al. 2023

Energy Exploration & Exploitation

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Traditional machine learning methods are difficult to accurately forecast oil production when development measures change. A method of oil reservoir production prediction based on normalized mutual information and a long short-term memory-based sequence-to-sequence model (Seq2Seq-LSTM) was proposed to forecast reservoir production considering the influence of liquid production and well spacing density. First, the marine sandstone reservoirs in the Y basin were taken as the research object to establish the sample database. Then, the feature selection was carried out according to the normalized mutual information, and liquid production, production time, equivalent well spacing density, fluidity and original formation pressure were determined as input features. Finally, a Seq2Seq-LSTM model was established to forecast reservoir production by learning the interaction from multiple samples and multiple sequences, and mining the relationship between oil production and features. The research showed that the model has a high accuracy of production prediction and can forecast the change of production when the liquid production and well spacing density change, which can provide scientific recommendations to help the oilfield develop and adjust efficiently.

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Section: Normalized Mutual Informationmentioning

confidence: 99%

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

Cui,

Qian,

et al. 2023

Energy Exploration & Exploitation

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“…In engineering practice, extensive modification of chassis parameters is costly and time-consuming, and it is often the most cost-effective method to realize road noise performance improvement by modifying sensitive parameters on the critical path. This paper first performs a road noise sensitivity analysis using the CNN-SVR hybrid model in conjunction with the Mean Impact Value (MIV) algorithm [43]. Designable parameters, such as the dynamic stiffness of the chassis bushing and the damping force of the shock absorber, are perturbed within a specific range (±10%), and the impact of these perturbations on road noise is calculated.…”

Section: Srn Improving and Validationmentioning

confidence: 99%

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Jia,

Zhou,

Huang

et al. 2023

Electronics

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In order to enhance the predictive accuracy and control capabilities pertaining to low- and medium-frequency road noise in automotive contexts, this study introduces a methodology for Structural-borne Road Noise (SRN) prediction and optimization. This approach relies on a multi-level target decomposition and a hybrid model combining Convolutional Neural Network (CNN) and Support Vector Regression (SVR). Initially, a multi-level target analysis method is proposed, grounded in the hierarchical decomposition of vehicle road noise along the chassis parts, delineated layer by layer, in accordance with the vibration transmission path. Subsequently, the CNN–SVR hybrid model, predicated on the multi-level target framework, is proposed. Notably, the hybrid model exhibits a superior predictive accuracy exceeding 0.97, surpassing both traditional CNN and SVR models. Finally, the method and model are deployed for sensitivity analysis of chassis parameters in relation to road noise, as well as for the prediction and optimization analysis of SRN in vehicles. The outcomes underscore the high sensitivity of parameters such as the dynamic stiffness of the rear axle bushing and the large front swing arm bushing influencing SRN. The optimization results, facilitated by the CNN–SVR hybrid model, align closely with the measured outcomes, displaying a negligible relative error of 0.82%. Furthermore, the measured results indicate a noteworthy enhancement of 4.07% in the driver’s right-ear Sound Pressure Level (SPL) following the proposed improvements compared to the original state.

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“…A support vector machine (SVM) represents a supervised learning algorithm and serves as a classification prediction model that operates on statistical principles [31]. Generally speaking, its fundamental principle involves assigning a group of training samples into two categories.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

Xue

et al. 2023

Land

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Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS.

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Feature Selection Method Based on Mutual Information and Support Vector Machine

Cited by 15 publications

References 13 publications

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

Improving Electric Vehicle Structural-Borne Noise Based on Convolutional Neural Network-Support Vector Regression

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

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