Impact of Labeling Noise on Machine Learning: A Cost-aware Empirical Study.
Abdulrahman Ahmed GharawiJumana AlsubhiLakshmish RamaswamyPublished in: ICMLA (2022)
Keyphrases
- empirical studies
- machine learning
- active learning
- empirical analysis
- real world data sets
- random noise
- cost sensitive
- uci datasets
- computer vision
- high cost
- experimental design
- inductive learning
- noise reduction
- noisy data
- cost sensitive learning
- noise free
- missing data
- total cost
- machine learning methods
- unsupervised learning
- image segmentation
- pattern recognition
- computational intelligence
- noise model
- additive noise
- decision trees
- natural language processing
- artificial intelligence
- knowledge acquisition
- cost benefit
- data sets
- systematic review
- knowledge discovery
- feature selection
- image noise
- learning algorithm
- machine learning algorithms
- support vector machine
- supervised learning
- text classification
- learning problems
- statistical methods
- learning systems