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SDDSMOTE: Synthetic Minority Oversampling Technique based on Sample Density Distribution for Enhanced Classification on Imbalanced Microarray Data.
Qikang Wan
Xiongshi Deng
Min Li
Haotian Yang
Published in:
ICCDA (2022)
Keyphrases
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microarray data
class imbalance
minority class
microarray data analysis
density distribution
feature selection
microarray
gene selection
dna microarray data
gene expression
microarray datasets
cancer classification
class distribution
class imbalanced
small number of samples
dna microarray
gene expression profiles
support vector machine
gene expression data
ovarian cancer
microarray analysis
support vector machine svm
high dimensionality
data sets
cluster analysis
text classification
training set
pattern recognition
machine learning
arbitrary shape
training samples
model selection
feature space
support vector
feature extraction
cost sensitive learning
decision boundary
random forest
gene sets
cost sensitive
cancer diagnosis
machine learning methods
supervised learning
knn