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Hierarchical Novelty Detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Publisher or commissioning bodySpringer Verlag
Number of pages12
ISBN (Electronic)9783319687650
ISBN (Print)9783319687643
DateAccepted/In press - 14 Jul 2017
DatePublished (current) - 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
CountryUnited Kingdom


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.

    Research areas

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


16th International Symposium on Intelligent Data Analysis, IDA 2017

Duration26 Oct 201728 Oct 2017
CountryUnited Kingdom

Event: Conference



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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 834 KB, PDF-document

    Embargo ends: 4/10/18

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