Evaluating feature selection for stress identification

Keyphrases

• Feature Selection 100%
• Stress Identification 100%
• Stress Level 66%
• Principal Coordinate Analysis (PCoA) 33%
• Preparation Approach 33%
• Extracting Features 33%
• Paired T-test 33%
• Computation Time 33%
• Health Condition 33%
• Statistical Significance 33%
• C4.5 33%
• Human Being 33%
• Support Vector Machine 33%
• Modernity 33%
• Feature Selection Methods 33%
• Media Labs 33%
• Poor Health 33%
• Classification Algorithms 33%
• Correct Rate 33%
• K-nearest Neighbor (K-NN) 33%
• Tree Support 33%
• Linear Discriminant Function 33%
• Physiological Sensors 33%
• Nave Bayes 33%
• Cross-validation Method 33%
• Data Preparation 33%
• Biosignals 33%

Computer Science

• Feature Selection 100%
• Feature Extraction 100%
• Experimental Result 25%
• Support Vector Machine 25%
• Component Analysis 25%
• Principal Components 25%
• Health Condition 25%
• Discriminant Function 25%
• Physiological Sensor 25%
• Linear Discriminant 25%
• Nave Bayes 25%
• Classification Algorithm 25%
• Computational Time 25%
• Data Preparation 25%