Improving SVM Classification on Imbalanced Datasets for EEG-Based Person Authentication

2019 ◽  
pp. 57-66
Author(s):  
Nga Tran ◽  
Dat Tran ◽  
Shuangzhe Liu ◽  
Linh Trinh ◽  
Tien Pham
Keyword(s):  
Imbalanced Datasets ◽  
Svm Classification

scholarly journals AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xin Wang ◽  
Yue Yang ◽  
Mingsong Chen ◽  
Qin Wang ◽  
Qin Qin ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hierarchical Clustering ◽  
Classification Accuracy ◽  
Classification Performance ◽  
Support Vector ◽  
Seed Sample ◽  
Imbalanced Datasets ◽  
Centroid Method ◽  
Svm Classification ◽  
Sampling Process

Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm—AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE—is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority subclusters on the basis of the obtained majority subclusters; select “seed sample” based on the sampling weight and probability distribution of minority subcluster; and restrict the generation of new samples in a certain area by centroid method in the sampling process. The combination of AGNES-SMOTE and SVM (Support Vector Machine) is presented to deal with imbalanced datasets classification. Experiments on UCI datasets are conducted to compare the performance of different algorithms mentioned in the literature. Experimental results indicate AGNES-SMOTE excels in synthesizing new samples and improves SVM classification performance on imbalanced datasets.

scholarly journals Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias

2017 ◽  
Vol 34 (3) ◽  
pp. 427-443 ◽  
Author(s):  
Haydemar Núñez ◽  
Luis Gonzalez-Abril ◽  
Cecilio Angulo
Keyword(s):  
Imbalanced Datasets ◽  
Svm Classification

Self-localization algorithm for sensor networks using SVM classification region

2009 ◽  
Vol 29 (4) ◽  
pp. 1064-1067 ◽  
Author(s):  
Ming LIU ◽  
Ting-ing WANG ◽  
Xiao-an HUANG ◽  
Rui LIU
Keyword(s):  
Sensor Networks ◽  
Localization Algorithm ◽  
Svm Classification
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