[HTML][HTML] Least square-support vector machine based brain tumor classification system with multi model texture features
Radiologists confront formidable challenges when confronted with the intricate task of
classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript …
classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript …
Improved machine learning leak fault recognition for low-pressure natural gas valve
M Liu, X Lang, S Li, L Deng, B Peng, Y Wu… - Process Safety and …, 2023 - Elsevier
Monitoring valve operation status is very significant in saving natural gas resources and
realizing sustainability of the fossil energy. At present, many machine learning algorithms …
realizing sustainability of the fossil energy. At present, many machine learning algorithms …
Bell-shaped fuzzy least square twin SVM with biomedical applications
In practical applications, datasets frequently encompass noise, outliers, and imbalanced
classes, all of which can markedly affect the generalization performance of the model …
classes, all of which can markedly affect the generalization performance of the model …
Class Probability and Generalized Bell Fuzzy Twin SVM for Imbalanced Data
The data mining community has a major challenge in classifying datasets with noise,
outliers, and imbalanced classes. Twin support vector machine (TSVM) is a well-known …
outliers, and imbalanced classes. Twin support vector machine (TSVM) is a well-known …
Curriculum learning-based fuzzy support vector machine
B Chen, Y Gao, J Liu, W Weng, J Huang… - … on Fuzzy Systems, 2023 - ieeexplore.ieee.org
To improve the robustness of SVM models to noise and outliers, fuzzy support vector
machine (FSVM) has been proposed. However, many existing FSVM models have …
machine (FSVM) has been proposed. However, many existing FSVM models have …
Dual center based intuitionistic fuzzy plane based classifiers
The plane-based classifiers, support vector machine (SVM) and twin support vector machine
(TWSVM), are susceptible to the negative impact of noise, outliers, and class imbalance …
(TWSVM), are susceptible to the negative impact of noise, outliers, and class imbalance …
[HTML][HTML] Cluster-based oversampling with area extraction from representative points for class imbalance learning
Class imbalance learning is challenging in various domains where training datasets exhibit
disproportionate samples in a specific class. Resampling methods have been used to adjust …
disproportionate samples in a specific class. Resampling methods have been used to adjust …