Hierarchical Classification using Ranked Skip Connections

• Designed innovative neural architectures for hierarchical classification, improving prediction accuracy by 2% at finer levels and 15% at coarser levels compared to prior techniques
• Reduced disparity between predictions across levels of bird taxonomy hierarchy by applying an ensemble learning approach, resulting in a 12% increase in overall classification accuracy