Detection of outliers in classification by using quantified uncertainty in neural networks.
Magnus MalmströmIsaac SkogDaniel AxehillFredrik GustafssonPublished in: FUSION (2022)
Keyphrases
- neural network
- pattern recognition
- automatic classification
- microcalcification clusters
- decision trees
- robust detection
- feature extraction and classification
- novelty detection
- automatic detection
- classification algorithm
- neyman pearson
- mammogram images
- digital mammograms
- classification models
- detection method
- detection algorithm
- data mining
- machine learning
- support vector machine
- image classification
- object detection
- decision rules
- feature selection
- anomaly detection
- learning vector quantization
- training set
- feature space
- neural networks and support vector machines
- support vector data description
- false alarms
- genetic algorithm
- feature extraction
- support vector
- classification scheme
- training process
- artificial neural networks
- feature vectors
- detection rate
- breast cancer
- classification rules
- missing values
- classification method
- support vector machine svm