Subtree classification hierarchical rule
The subtree classification rule enables iterative classification inside a subtree using different threshold values for each
level. Once
subtree classification is
enabled, you can specify lower thresholds for minimum confidence and distance.
If the parent is selected as the classification result during production, an additional subtree evaluation step is performed that applies the parent threshold values to the children. If those thresholds are not met, the parent is used as the final classification result.
The following example shows that aristotle has the highest confidence, and as such, would normally become the classification result after the first step. But, aristotle also has the subtree classification option enabled with a threshold of 30% for the minimum confidence and 5% for the minimum distance settings. Due to this lower value, aristotle_politics, with 40% confidence, becomes the final classification result. The confidence of 60% for plato_republic does not matter here, because only classes inside the subtree below aristotle are considered during this additional step.
