Probabilistic Modeling of Progressive Filtering

TL;DR

This paper models progressive filtering in hierarchical text categorization using probabilistic methods, aiming to assist system designers in development, training, and testing by providing a formal framework.

Contribution

It introduces a probabilistic model for progressive filtering, enhancing understanding and implementation in hierarchical classification systems.

Findings

Provides a formal probabilistic framework for progressive filtering

Facilitates system design, training, and testing processes

Improves interpretability of hierarchical classification

Abstract

Progressive filtering is a simple way to perform hierarchical classification, inspired by the behavior that most humans put into practice while attempting to categorize an item according to an underlying taxonomy. Each node of the taxonomy being associated with a different category, one may visualize the categorization process by looking at the item going downwards through all the nodes that accept it as belonging to the corresponding category. This paper is aimed at modeling the progressive filtering technique from a probabilistic perspective, in a hierarchical text categorization setting. As a result, the designer of a system based on progressive filtering should be facilitated in the task of devising, training, and testing it.