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“One vs All” Classifier Analysis for Multi-label Movie Genre Classification Using Document Embedding

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Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

Abstract

The classification of movie genres from their synopses has attracted the attention of many researchers. Indeed, synopses are a source of relevant information that contributes to determinate movie genre. The automation of this classification process is very useful in several applications, such recommendation systems. Moreover, movies can belong simultaneously to several genres (drama, action, comedy, horror), which reflects a typical problem of multi-label classification (MLC). In this article, we use a powerful representation of film synthesis via a document integration technique Doc2vec in the multi-label classification context. The technique used in our experience is One Vs All, which is a transformation approach; it creates a model for each label through a kernel classifier. We have chosen to use three different classifiers: logistic regression, SVM and ANN. The results of our experimental study show that the best accuracies are obtained using ANN model.

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Notes

  1. 1.

    https://www.cs.cmu.edu/~ark/personas/.

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Author information

Authors and Affiliations

  1. LRI Laboratory, Badji Mokhtar University, 12-23000, Annaba, BP, Algeria

    Sonia Guehria

  2. LabGED, Badji Mokhtar Annaba University, 12-23000, Annaba, BP, Algeria

    Habiba Belleili & Nabiha Azizi

  3. College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

    Samir Brahim Belhaouari

Authors
  1. Sonia Guehria
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  2. Habiba Belleili
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  3. Nabiha Azizi
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  4. Samir Brahim Belhaouari
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Corresponding author

Correspondence to Sonia Guehria .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

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Guehria, S., Belleili, H., Azizi, N., Belhaouari, S.B. (2021). “One vs All” Classifier Analysis for Multi-label Movie Genre Classification Using Document Embedding. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_44

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