Hierarchical partitioning of the output space in multi-label data

Yannis Papanikolaou, Grigorios Tsoumakas, Ioannis Katakis

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Hierarchy Of Multi-label classifiERs (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and secondly employing a given base multi-label classifier (MLC) to the resulting sub-problems. The primary goal is to effectively address class imbalance and scalability issues that often arise in real-world multi-label classification problems. In this work, we present the general setup for a HOMER model and a simple extension of the algorithm that is suited for MLCs that output rankings. Furthermore, we provide a detailed analysis of the properties of the algorithm, both from an aspect of effectiveness and computational complexity. A secondary contribution involves the presentation of a balanced variant of the k means algorithm, which serves in the first step of the label hierarchy construction. We conduct extensive experiments on six real-world data sets, studying empirically HOMER's parameters and providing examples of instantiations of the algorithm with different clustering approaches and MLCs, The empirical results demonstrate a significant improvement over the given base MLC.

Original languageEnglish
Pages (from-to)42-60
Number of pages19
JournalData and Knowledge Engineering
Volume116
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • Knowledge discovery
  • Machine learning
  • Supervised learning
  • Text mining

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