Application of Optimized SVM in Sample Classification

Yao, Xuemei

doi:10.14569/ijacsa.2022.0130666

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…For example, SVM exhibits significant advantages in addressing small sample problems. It maintains robust classification performance even with limited sample sizes by constructing an optimal hyperplane to separate different data categories ( Yao, 2022 ). RF enhances prediction accuracy and robustness by integrating multiple decision trees ( Momade et al, 2020 ), while XGBoost supports various objective functions and evaluation metrics, enabling outstanding performance in diverse prediction tasks ( Sagi & Rokach, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Soil organic carbon estimation using remote sensing data-driven machine learning

Chen,

Wang,

Zhu

2024

PeerJ

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Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use types, and explored its spatial distribution pattern and influencing factors. Machine learning methods including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were employed to estimate the surface SOC content in Shandong Province using diverse data sources like sample data, remote sensing data, socio-economic data, soil texture data, topographic data, and meteorological data. The results revealed that the SOC content in Shandong Province was 8.78 g/kg, exhibiting significant variation across different regions. Comparing the model error and correlation coefficient, the XGBoost model showed the highest prediction accuracy, with a coefficient of determination (R²) of 0.7548, root mean square error (RMSE) of 7.6792, and relative percentage difference (RPD) of 1.1311. Elevation and Clay exhibited the highest explanatory power in clarifying the surface SOC content in Shandong Province, contributing 21.74% and 13.47%, respectively. The spatial distribution analysis revealed that SOC content was higher in forest-covered mountainous regions compared to cropland-covered plains and coastal areas. In conclusion, these findings offer valuable scientific insights for land use planning and SOC conservation.

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Section: Methodsmentioning

confidence: 99%

Soil organic carbon estimation using remote sensing data-driven machine learning

Chen,

Wang,

Zhu

2024

PeerJ

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“…During the training and validation processes of our SVR model, penalty coefficient c and kernel function parameter g were determined using the K-fold cross-validation (K-CV) method to identify the optimal combination [57][58][59]. The K-value was equivalent to the number of groups in the partitioned training set.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Seepage Prediction Model for Roller-Compacted Concrete Dam Using Support Vector Regression and Hybrid Parameter Optimization

Zhuo,

Chen,

Zhang

et al. 2023

Water

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In this study, a seepage prediction model was established for roller-compacted concrete dams using support vector regression (SVR) with hybrid parameter optimization (HPO). The model includes data processing via HPO and machine learning through SVR. HPO benefits from the correlation extraction capability of grey relational analysis and the dimensionality reduction technique of principal component analysis. The proposed model was trained, validated, and tested using 22 years of monitoring data regarding the Shuidong Dam in China. We compared the performance of HPO with other popular methods, while the SVR method was compared with the traditional time-series prediction method of long short-term memory (LSTM). Our findings reveal that the HPO method proves valuable real-time dam safety monitoring during data processing. Meanwhile, the SVR method demonstrates superior robustness in predicting seepage flowrate post-dam reinforcement, compared with LSTM. Thus, the developed model effectively identifies the factors related to seepage and exhibits high accuracy in predicting fluctuation trends regarding the Shuidong Dam, achieving a determination coefficient R2 > 0.9. Further, the model can provide valuable guidance for dam safety monitoring, including diagnosing the efficacy of monitoring parameters or equipment, evaluating equipment monitoring frequency, identifying locations sensitive to dam seepage, and predicting seepage.

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“…On the other hand, whenever the data set is larger, it requires a lot of computational time. [16] 5.2 Naive Bayes(NB) [17] The NB, a widely used method, mathematically adheres to Bayes' theorem. The eq.14 shows the probability calculation using Bayes theorem.…”

Section: Support Vector Machine(svm)[15]mentioning

confidence: 99%

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

Dewan¹,

Thepade²,

Deshmukh³

et al. 2023

IJETT

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The detection of brain tumors using Magnetic Resonance Images(MRI) is difficult for contemporary medical imaging research. Basically, a brain tumor is an expansion of aberrant brain cells that expand erratically and seemingly uncontrolled. Meningioma, Glioma, and Pituitary are the three kinds of tumors that are most frequently seen. Early identification is essential for the successful treatment of brain tumors. With the development of medical imaging, doctors now employ a variety of imaging methods, such as fMRI, EEG, etc., to diagnose brain tumors. These imaging methods can help clinicians establish a precise diagnosis and create a treatment strategy by providing details on the location, size, and shape of brain tumors. Feature extraction and classification are two steps in the categorization of brain tumors. Two traditional manual feature extraction methods were frequently utilized in certain earlier research to extract details like the intensity and texture of images of brain tumors. This work employs the 'GLCM(Grey Level Co-occurrence Matrix)' approach for feature extraction. The generated feature set is provided to machine learning(ML) algorithms, including 'K-Nearest Neighbours(KNN), Support Vector Machine(SVM), Decision Tree(DT), Naive Bayes(NB), Logistic Regression(LR), Naive Bayes(NB), and Random Forest(RF)'. According to experimental results, random forest yields a maximum accuracy of 91.04%. The proposed methodology helps to classify the different brain tumor classes like glioma, meningioma, pituitary, or else no tumor.

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Application of Optimized SVM in Sample Classification

Cited by 5 publications

References 11 publications

Soil organic carbon estimation using remote sensing data-driven machine learning

Soil organic carbon estimation using remote sensing data-driven machine learning

Seepage Prediction Model for Roller-Compacted Concrete Dam Using Support Vector Regression and Hybrid Parameter Optimization

Brain Tumor Type Identification from MR Images Using Texture Features and Machine Learning Techniques

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