Predicting invasive species distributions using incremental ensemble-based pseudo-labeling

Keyphrases

• Ensemble-based 100%
• Species Distribution Models 100%
• Pseudo-labeling 100%
• Invasive Species Distribution Model 100%
• Training Set 80%
• Teacher Model 80%
• Prediction Accuracy 40%
• Labeled Data 40%
• Limited Labeled Data 40%
• Habitat Suitability 40%
• Machine Learning-based Approach 40%
• Invasive Species 40%
• Pseudo Label 40%
• Statistical Methods 20%
• Performance Improvement 20%
• Prediction Scheme 20%
• Control Strategy 20%
• Performance Prediction 20%
• Evaluation Metrics 20%
• Novel Prediction 20%
• Geographical Distribution 20%
• Unlabeled Data 20%
• State-of-the-art Models 20%
• Semi-supervised Method 20%
• Regulation Strategy 20%
• Potential Habitat 20%
• Incremental Labeling 20%
• Citizen Science 20%
• Species Distribution Predictions 20%

Computer Science

• Machine Learning-Based Approach 100%
• Prediction Accuracy 50%
• Control Strategy 50%
• And-States 50%
• Performance Improvement 50%
• Evaluation Metric 50%
• Semisupervised Learning 50%
• Unlabeled Data 50%
• Predictive Accuracy 50%
• Predictive Performance 50%
• Citizen Science 50%