# Probabilistic Cascading for Large Scale Hierarchical Classification

arXiv: https://arxiv.org/abs/1505.02251 · HTML: https://mlsanity.com/paper/1505.02251/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2015.

## Built on by (most cited first, 1 of 1)

- [Loss Functions for Classification using Structured Entropy](https://mlsanity.com/paper/2206.07122/) (2022)

## Abstract (arXiv)

Hierarchies are frequently used for the organization of objects. Given a hierarchy of classes, two main approaches are used, to automatically classify new instances: flat classification and cascade classification. Flat classification ignores the hierarchy, while cascade classification greedily traverses the hierarchy from the root to the predicted leaf. In this paper we propose a new approach, which extends cascade classification to predict the right leaf by estimating the probability of each root-to-leaf path. We provide experimental results which indicate that, using the same classification algorithm, one can achieve better results with our approach, compared to the traditional flat and cascade classifications.