Choosing Thresholds/Clusters in Neural network Classification
When working with neural networks, the output is often a probability distribution across different classes. To make a final classification decision, we need to convert these probabilities into discrete class labels. This is typically done by comparing the probabilities against a threshold or by clustering them.
Threshold-Based Classification
In threshold-based classification, we set a specific probability value as the threshold. If the probability of a class exceeds this threshold, the input is classified as belonging to that class. Otherwise, it’s classified as belonging to another class or as “unknown.”
Clustering-Based Classification
In clustering-based classification, we group the probability distributions into clusters. Each cluster represents a class. This approach is useful when the class boundaries are not well-defined or when there are multiple overlapping classes.
Choosing the Number of Clusters
Additional Considerations:
- Imbalanced Datasets: If the classes are imbalanced, the choice of threshold or number of clusters can be significantly affected. Techniques like oversampling, undersampling, or using weighted loss functions can help mitigate the impact of class imbalance.
- Data Quality: The quality of the training data can also influence the choice of threshold or number of clusters. If the data is noisy or contains outliers, the chosen values may not be optimal.
- Evaluation Metrics: Choose evaluation metrics that are appropriate for the specific problem and the desired trade-off between different types of errors.