A Comparative Performance Evaluation of Random Forest Feature Selection on Classification of Hepatocellular Carcinoma Gene Expression Data

Keyphrases

• 5-fold Cross Validation 20%
• Classification Algorithms 60%
• Classification Neural Networks 20%
• Classification Performance 20%
• Comparative Performance Evaluation 100%
• Evaluation Method 20%
• Feature Selection 60%
• Feature Selection Methods 20%
• Forest Logistics 20%
• Gene Expression 20%
• Gene Expression Data 100%
• Hepatocellular Carcinoma 100%
• High Performance 60%
• Higher Classification 20%
• Information Website 20%
• Logistic Regression 40%
• Logistic Regression Model 20%
• Machine Classification 20%
• Microarray Data 20%
• National Center for Biotechnology Information 20%
• Nave Bayes 20%
• Naïve Bayes 20%
• Neural Network 20%
• Neural Network Classifier 20%
• Performance Evaluation 100%
• Random Forest 20%
• Random Forest Algorithm 20%
• Random Forest Feature Selection 100%
• Support Vector Classification 40%
• Vector Machine 20%

Computer Science

• Artificial Neural Network 14%
• Classification Algorithm 42%
• Classification Performance 14%
• Feature Selection 100%
• Fold Cross Validation 14%
• Gene Expression Data 100%
• Logistic Regression 28%
• Logistic Regression Model 14%
• Microarray Data 14%
• Nave Bayes 14%
• Neural Network 14%
• Performance Evaluation 100%
• Random Decision Forest 100%
• Support Vector 28%

Biochemistry, Genetics and Molecular Biology

• Bayesian Learning 16%
• Classification Algorithm 50%
• Gene Expression Data 100%
• Microarrays 16%
• Random Forest 100%
• Support Vector Machine 33%