Probabilistic Cascading for Large Scale Hierarchical Classification

TL;DR

This paper introduces a probabilistic cascading approach for large-scale hierarchical classification, improving accuracy by estimating path probabilities, outperforming traditional flat and cascade methods.

Contribution

It extends cascade classification by incorporating probability estimates for root-to-leaf paths, enhancing classification accuracy in hierarchical structures.

Findings

Better accuracy than flat classification

Improved results over traditional cascade classification

Effective with the same classification algorithm

Abstract

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.