Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Xie, Su; Li, Ké; Xiao, Ming; Zhang, Le; Li, Wanlin

doi:10.3390/app10062141

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…With embedded methods, feature selection is performed during the model learning process [37][38][39]. In other words, feature selection is incorporated into the classifier training process.…”

Section: Embedded Methodsmentioning

confidence: 99%

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Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

et al. 2020

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Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy.

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Section: Embedded Methodsmentioning

confidence: 99%

“…The data collection process and the features were assessed from the associated factors are shown in Figure 1. With embedded methods, feature selection is performed during the model learning process [37][38][39]. In other words, feature selection is incorporated into the classifier training process.…”

Section: Instrumentsmentioning

confidence: 99%

Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

et al. 2020

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“…ReliefF operates by randomly selecting samples and then examining the nearest similar (neighbor) and dissimilar samples, determining each feature's contribution to the correct classification of the samples. Features are deemed important based on their ability to distinguish neighboring samples, particularly those that effectively differentiate between various categories [33]. ReliefF's principal advantage over other feature selection techniques is its robustness against noise and irrelevant features in data, along with its ability to manage different data types, including both discrete and continuous features.…”

Section: Relieffmentioning

confidence: 99%

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

Du,

He,

Luo

et al. 2024

Sustainability

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In order to investigate the mechanism of environmental factors in soil salinization, this study focused on analyzing the temporal-spatial variation of soil salinity in the Ogan-Kuqa River Oasis in Xinjiang, China. The research aimed to predict soil salinity using a combination of satellite data, environmental covariates, and advanced modeling techniques. Firstly, Boruta and ReliefF algorithms were employed to select variables that significantly affect soil salinity from the Sentinel-2 satellite data and environmental covariates. Subsequently, a soil salinity inversion model was established using three advanced strategies: comprehensive variable analysis, a Boruta-based variable selection algorithm, and a ReliefF-based variable selection algorithm. Each strategy was modeled using a Light Gradient Boosting Machine (LightGBM), an Extreme Learning Machine (ELM), and a Support Vector Machine (SVM). Finally, the Boruta-LightGBM strategy was proven to be the most effective in predicting soil electrical conductivity (EC), with a coefficient of determination (R2) of 0.72 and a Root Mean Square Error (RMSE) of 12.49 ds/m. The experimental results show that the red-edge band index is the foremost variable in predicting soil salinity, succeeded by the salinity index and soil attribute data, while the topographic index has the least influence, which further demonstrates that proper variable selection could significantly improve model functionality and predictive precision. Furthermore, the Multiscale Geographically Weighted Regression (MGWR) model was utilized to reveal the influence and temporal-temporal-spatial heterogeneity of environmental factors such as soil organic carbon (SOC), precipitation (PRE), pH value, and temperature (TEM) on soil EC. This research offers not just a viable methodological framework for monitoring soil salinization but also new perspectives on the environmental drivers of soil salinity changes, which have implications for sustainable land management and provide valuable information for decision-making in soil salinity control and mitigation efforts.

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Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

Chen

Pan

et al. 2022

Electronics

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With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

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Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection

Cited by 3 publications

References 17 publications

Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China

Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

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