Machine Learning Approach for Classifying College Scholastic Ability Test Levels With Unsupervised Features From Prefrontal Functional Near-Infrared Spectroscopy Signals.
Jung-Gu ChoiInhwan KoYoonjin NahBora KimYongwan ParkJihyun ChaJongkwan ChoiSanghoon HanPublished in: IEEE Access (2022)
Keyphrases
- machine learning
- image features
- low level
- machine learning approaches
- supervised learning
- computer vision
- feature vectors
- unsupervised learning
- unsupervised feature selection
- abstraction levels
- natural language processing
- levels of abstraction
- feature set
- supervised classification
- feature engineering
- learning tasks
- signal processing
- support vector machine
- data mining
- knowledge acquisition
- model selection
- co occurrence
- key features
- semi supervised
- quantitative analysis
- inductive learning
- classification accuracy
- data analysis
- reinforcement learning
- feature generation
- artificial intelligence
- learning algorithm
- learning methodologies