# Performance Improvement in Multi-class Classification via Automated Hierarchy Generation and Exploitation through Extended LCPN Schemes

arXiv: https://arxiv.org/abs/2310.20641 · HTML: https://mlsanity.com/paper/2310.20641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [Inducing a hierarchy for multi-class classification problems](https://mlsanity.com/paper/2102.10263/) (2021)
- [MMF: Multi-Task Multi-Structure Fusion for Hierarchical Image Classification](https://mlsanity.com/paper/2107.00808/) (2021)
- [Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes](https://mlsanity.com/paper/1406.4781/) (2014)
- [Pitfalls of Assessing Extracted Hierarchies for Multi-Class Classification](https://mlsanity.com/paper/2101.11095/) (2021)

## Abstract (arXiv)

Hierarchical classification (HC) plays a pivotal role in multi-class classification tasks, where objects are organized into a hierarchical structure. This study explores the performance of HC through a comprehensive analysis that encompasses both hierarchy generation and hierarchy exploitation. This analysis is particularly relevant in scenarios where a predefined hierarchy structure is not readily accessible. Notably, two novel hierarchy exploitation schemes, LCPN+ and LCPN+F, which extend the capabilities of LCPN and combine the strengths of global and local classification, have been introduced and evaluated alongside existing methods. The findings reveal the consistent superiority of LCPN+F, which outperforms other schemes across various datasets and scenarios. Moreover, this research emphasizes not only effectiveness but also efficiency, as LCPN+ and LCPN+F maintain runtime performance comparable to Flat Classification (FC). Additionally, this study underscores the importance of selecting the right hierarchy exploitation scheme to maximize classification performance. This work extends our understanding of HC and establishes a benchmark for future research, fostering advancements in multi-class classification methodologies.