Improving the performance of inductive learning classifiers through the presentation order of the training patterns

Keyphrases

• Training Pattern 100%
• Inductive Learning 100%
• Learning Classifiers 100%
• Order of Presentation 100%
• Order Methods 40%
• Benchmark Dataset 20%
• Machine Learning Techniques 20%
• Classification Accuracy 20%
• Predictive Power 20%
• Direct Impact 20%
• Interpretable Classification 20%
• Classification Rules 20%
• Preprocessing Techniques 20%
• Predictive Classification 20%
• Systems Context 20%
• Capacity to Understand 20%
• Inductive Learning Algorithm 20%
• Density Measure 20%
• Supervised Learning Algorithms 20%
• Expert System 20%
• Classification Process 20%
• Generalization Power 20%
• If-then Rules 20%
• Real Application 20%
• Training Examples 20%
• Wood Defect Classification 20%

INIS

• learning 100%
• performance 100%
• classification 100%
• applications 50%
• algorithms 50%
• power 50%
• density 25%
• datasets 25%
• capacity 25%
• defects 25%
• accuracy 25%
• benchmarks 25%
• machine learning 25%
• randomness 25%
• industry 25%
• wood 25%
• expert systems 25%
• business 25%
• depth 25%

Computer Science

• Learning Algorithm 100%
• Classification Accuracy 50%
• Machine Learning Technique 50%
• Predictive Power 50%
• Training Example 50%
• Classification Rule 50%
• Classification Process 50%
• Expert System 50%
• Supervised Learning 50%
• Preprocessing Stage 50%
• Real Application 50%