An Effective Machine Learning Approach for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features

Engineering

• Learning Approach 100%
• Time Domain 100%
• Domain Feature 100%
• Support Vector Machine 100%
• Classification Performance 66%
• Absolute Deviation 66%
• Feature Extraction 33%
• Frequency Domain 33%
• Main Advantage 33%
• Receiver Operating Characteristic 33%
• Operating-Characteristic Curve 33%
• Computational Time 33%
• Radial Basis Function 33%
• Random Forest 33%
• Machine Learning Algorithm 33%
• Fourth Order 33%
• Pearsons Linear Correlation Coefficient 33%
• Phase Classification 33%

Computer Science

• Support Vector Machine 100%
• Domain Feature 100%
• Machine Learning Approach 100%
• Classification Performance 66%
• Decision Trees 33%
• Machine Learning Algorithm 33%
• Open Research 33%
• Kernel Function 33%
• Feature Extraction 33%
• Frequency Domain 33%
• Automatic Classification 33%
• Random Decision Forest 33%
• Radial Basis Function 33%
• Research Problem 33%
• Fold Cross Validation 33%
• Analysis Signal 33%
• Computational Time 33%
• classification approach 33%
• Pearson Correlation Coefficient 33%
• Characteristic Curve 33%