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Learning From Imbalanced Data With Deep Density Hybrid Sampling
Chien-Liang Liu
*
, Yu-Hua Chang
*
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工業工程與管理學系
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引文 斯高帕斯(Scopus)
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Keyphrases
Learning from Imbalanced Data
100%
Hybrid Sampling
100%
Synthetic Samples
40%
Imbalanced Data
40%
Euclidean Distance
40%
Minority Class
40%
Feature Space
20%
Machine Learning
20%
Boosting Method
20%
Loss Function
20%
Nearest Neighbor
20%
Level Set Algorithm
20%
High-dimensional Space
20%
Latent Space
20%
Distance Metric
20%
Embedded Networks
20%
Ensemble Methods
20%
Majority Class
20%
Synthetic Minority Oversampling Technique (SMOTE)
20%
Feature Level
20%
Data Projection
20%
Minority Sample
20%
Computer Science
Imbalanced Data
100%
Hybrid Sampling
100%
Euclidean Distance
40%
Minority Class
40%
Experimental Result
20%
Feature Space
20%
Boosting Technique
20%
Distance Metric
20%
Majority Class
20%
Ensemble Method
20%
Projection Data
20%
Machine Learning
20%
Learning System
20%
High Dimensional Space
20%
Mathematics
Euclidean Distance
100%
Loss Function
50%
Dimensional Space
50%
Feature Space
50%
Nearest Neighbor
50%
Oversampling
50%
Data Sample
50%
Majority Class
50%