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