Hierarchical Novelty Detection

Paolo Simeone*, Raúl Santos-Rodríguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

4 Citations (Scopus)
251 Downloads (Pure)


Hierarchical classification is commonly defined as multi-class classification where the classes are hierarchically nested. Many practical hierarchical classification problems also share features with multi-label classification (i.e., each data point can have any number of labels, even non-hierarchically related) and novelty detection (i.e., some data points are novelties at some level of the hierarchy). A further complication is that it is common for training data to be incompletely labelled, e.g. the most specific labels are not always provided. In music genre classification for example, there are numerous music genres (multi-class) which are hierarchically related. Songs can belong to different (even non-nested) genres (multi-label), and a song labelled as Rock may not belong to any of its sub-genres, such that it is a novelty within this genre (novelty-detection). Finally, the training data may label a song as Rock whereas it really could be labelled correctly as the more specific genre Blues Rock. In this paper we develop a new method for hierarchical classification that naturally accommodates every one of these properties. To achieve this we develop a novel approach, modelling it as a Hierarchical Novelty Detection problem that can be trained through a single convex second-order cone programming problem. This contrasts with most existing approaches that typically require a model to be trained for each layer or internal node in the label hierarchy. Empirical results on a music genre classification problem are reported, comparing with a state-of-the-art method as well as simple benchmarks.

Original languageEnglish
Title of host publicationAdvances in Intelligent Data Analysis XVI
Subtitle of host publication16th International Symposium, IDA 2017, London, UK, October 26–28, 2017, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Electronic)9783319687650
ISBN (Print)9783319687643
Publication statusPublished - 4 Oct 2017
Event16th International Symposium on Intelligent Data Analysis, IDA 2017 - London, United Kingdom
Duration: 26 Oct 201728 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Intelligent Data Analysis, IDA 2017
Country/TerritoryUnited Kingdom


  • Hierarchical classification
  • Music genre classification
  • Music information retrieval
  • Novelty detection
  • Optimization


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