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

TL;DR

This paper introduces novel hierarchy exploitation schemes, LCPN+ and LCPN+F, that enhance multi-class classification performance by combining global and local methods, with LCPN+F showing consistent superiority.

Contribution

The study proposes and evaluates two new hierarchy exploitation schemes, LCPN+ and LCPN+F, extending existing methods and demonstrating improved accuracy and efficiency in hierarchical classification.

Findings

LCPN+F outperforms other schemes across datasets.

LCPN+ and LCPN+F maintain runtime similar to flat classification.

Selecting the right scheme significantly improves classification performance.

Abstract

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.