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Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features

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Book cover Semantic Keyword-Based Search on Structured Data Sources (IKC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9398))

Abstract

In this paper, we present a comparative study of news documents classification using various supervised machine learning methods and different combinations of key-phrases (word N-grams extracted from text) and visual features (extracted from a representative image from each document). The application domain is news documents written in English that belong to four categories: Health, Lifestyle-Leisure, Nature-Environment and Politics. The use of the N-gram textual feature set alone led to an accuracy result of 81.0 %, which is much better than the corresponding accuracy result (58.4 %) obtained through the use of the visual feature set alone. A competition between three classification methods, a feature selection method, and parameter tuning led to improved accuracy (86.7 %), achieved by the Random Forests method.

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Acknowledgments

This work was supported by MULTISENSOR project, partially funded by the European Commission, under the contract number FP7-610411. The authors would also like to thank Avi Rosenfeld, Maor Tzidkani and Daniel Nissim Cohen from the Jerusalem College of Technology, Lev Academic Center, for their assistance to the authors in providing the software tool to generate the textual features used in this research. The authors would also like to acknowledge the networking support by the COST Action IC1302: semantic KEYword-based Search on sTructured data sOurcEs (KEYSTONE) and the COST Action IC1307: The European Network on Integrating Vision and Language (iV&L Net).

Author information

Authors and Affiliations

  1. Department of Computer Science, Jerusalem College of Technology - Lev Academic Center, 9116001, Jerusalem, Israel

    Yaakov HaCohen-Kerner & Asaf Sabag

  2. Centre for Research and Technology Hellas, Information Technologies Institute, Thermi, Thessaloniki, Greece

    Dimitris Liparas, Anastasia Moumtzidou, Stefanos Vrochidis & Ioannis Kompatsiaris

Authors
  1. Yaakov HaCohen-Kerner
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  2. Asaf Sabag
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  3. Dimitris Liparas
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  4. Anastasia Moumtzidou
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  5. Stefanos Vrochidis
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  6. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Yaakov HaCohen-Kerner .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Cardoso

  2. Huawei European Research Center, Munich, Germany

    Jorge Cardoso

  3. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Francesco Guerra

  4. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Geert-Jan Houben

  5. University of Coimbra, Coimbra, Portugal

    Alexandre Miguel Pinto

  6. Università degli Studi di Trento, Trento, Italy

    Yannis Velegrakis

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HaCohen-Kerner, Y., Sabag, A., Liparas, D., Moumtzidou, A., Vrochidis, S., Kompatsiaris, I. (2015). Classification Using Various Machine Learning Methods and Combinations of Key-Phrases and Visual Features. In: Cardoso, J., Guerra, F., Houben, GJ., Pinto, A.M., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2015. Lecture Notes in Computer Science(), vol 9398. Springer, Cham. https://doi.org/10.1007/978-3-319-27932-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-27932-9_6

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