An ensemble classifier with random projection for predicting multi-label protein subcellular localization

Keyphrases

• Random Projection 100%
• Multi-label Protein Subcellular Localization 100%
• Ensemble Classifier 100%
• Projection Matrix 50%
• Reduction Method 25%
• Dimensionality Reduction 25%
• Protein Subcellular Localization 25%
• Number of Features 25%
• Classification Performance 25%
• One-vs-One 25%
• Number of Data 25%
• Irrelevant Information 25%
• Low-dimensional Space 25%
• Frequency of Occurrence 25%
• Prediction System 25%
• Feature Vector 25%
• Gene Ontology Terms 25%
• Protein Subcellular Localization Prediction 25%
• Redundant Information 25%
• Multi-label Classifier 25%
• Overfitting 25%
• Low-dimensional Vectors 25%
• Support Vector Classifier 25%
• Zero Mean 25%

Mathematics

• Projection Matrix 100%
• Data Sample 50%
• Dimensional Space 50%
• Dimensionality Reduction 50%
• Reduction Method 50%
• Support Vector Machine 50%
• Dimensional Vector 50%
• Feature Vector 50%
• Mean Variance 50%
• Unit Variance 50%

Computer Science

• Random Projection 100%
• ensemble classifier 100%
• Projection Matrix 50%
• Experimental Result 25%
• Feature Vector 25%
• Lower Dimensional Space 25%
• Ontology 25%
• Classification Performance 25%
• Dimensionality Reduction 25%
• Dimensional Vector 25%
• Final Decision 25%
• Support Vector Machine 25%