ShuffleFormer: An efficient shuffle meta framework for automatic modulation classification.
Jitong MaYin JingZhengyan YangHongjuan YangZhanjun WuPublished in: Phys. Commun. (2023)
Keyphrases
- pattern recognition
- meta level
- main contribution
- feature selection
- classification method
- classification systems
- theoretical framework
- image classification
- probabilistic model
- automatic classification
- lightweight
- classification accuracy
- training set
- ensemble classifier
- feature extraction
- pattern classification
- multi category
- classification process
- features extraction
- data sets
- fully automatic
- machine learning methods
- benchmark datasets
- support vector machine svm
- semi supervised learning
- support vector machine
- preprocessing
- decision trees
- machine learning