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Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships

  • Guillaume Bosc
  • Jérôme Golebiowski
  • Moustafa Bensafi
  • Céline Robardet
  • Marc Plantevit
  • Jean-François Boulicaut
  • Mehdi Kaytoue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9956)

Abstract

From a molecule to the brain perception, olfaction is a complex phenomenon that remains to be fully understood in neuroscience. A challenge is to establish comprehensive rules between the physicochemical properties of the molecules (e.g., weight, atom counts) and specific and small subsets of olfactory qualities (e.g., fruity, woody). This problem is particularly difficult as the current knowledge states that molecular properties only account for \(30\,\%\) of the identity of an odor: predictive models are found lacking in providing universal rules. However, descriptive approaches enable to elicit local hypotheses, validated by domain experts, to understand the olfactory percept. Based on a new quality measure tailored for multi-labeled data with skewed distributions, our approach extracts the top-k unredundant subgroups interpreted as descriptive rules \(description \rightarrow \{subset\ of\ labels\}\). Our experiments on benchmark and olfaction datasets demonstrate the capabilities of our approach with direct applications for the perfume and flavor industries.

Keywords

Search Space Jaccard Index Minimum Support Threshold Subgroup Discovery Odorant Molecule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research is partially supported by the CNRS (Préfute PEPS FASCIDO) and the Institut rhônalpin des systémes complexes (IXXI).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guillaume Bosc
    • 1
  • Jérôme Golebiowski
    • 3
  • Moustafa Bensafi
    • 4
  • Céline Robardet
    • 1
  • Marc Plantevit
    • 2
  • Jean-François Boulicaut
    • 1
  • Mehdi Kaytoue
    • 1
  1. 1.Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205LyonFrance
  2. 2.Université de Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205LyonFrance
  3. 3.Université de Nice, CNRS, Institute of ChemistryNiceFrance
  4. 4.Université de Lyon, CNRS, CRNL, UMR5292, INSERM U1028LyonFrance

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