Classification using various machine learning methods and combinations of key-phrases and visual features

Yaakov Hacohen-Kerner; Asaf Sabag; Dimitris Liparas; Anastasia Moumtzidou; Stefanos Vrochidis; Ioannis Kompatsiaris

doi:10.1007/978-3-319-27932-9_6

Classification using various machine learning methods and combinations of key-phrases and visual features

Yaakov Hacohen-Kerner, Asaf Sabag, Dimitris Liparas, Anastasia Moumtzidou, Stefanos Vrochidis, Ioannis Kompatsiaris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers
Editors	Yannis Velegrakis, Jorge Cardoso, Jorge Cardoso, Alexandre Miguel Pinto, Francesco Guerra, Geert-Jan Houben
Publisher	Springer Verlag
Pages	64-75
Number of pages	12
ISBN (Print)	9783319279312
DOIs	https://doi.org/10.1007/978-3-319-27932-9_6
State	Published - 2015
Externally published	Yes
Event	1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015 - Coimbra, Portugal Duration: 8 Sep 2015 → 9 Sep 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9398
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015
Country/Territory	Portugal
City	Coimbra
Period	8/09/15 → 9/09/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Funding

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).

Funders	Funder number
KEYSTONE	IC1307
MULTISENSOR
European Commission	FP7-610411
European Cooperation in Science and Technology
Jerusalem College of Technology - Lev Academic Center

Keywords

Document classification
Feature selection
Key-phrases
N-gram features
Supervised learning
Visual features

Access to Document

10.1007/978-3-319-27932-9_6

Cite this

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 Y. Velegrakis, J. Cardoso, J. Cardoso, A. M. Pinto, F. Guerra, & G.-J. Houben (Eds.), Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers (pp. 64-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9398). Springer Verlag. https://doi.org/10.1007/978-3-319-27932-9_6

Hacohen-Kerner, Yaakov ; Sabag, Asaf ; Liparas, Dimitris et al. / Classification using various machine learning methods and combinations of key-phrases and visual features. Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. editor / Yannis Velegrakis ; Jorge Cardoso ; Jorge Cardoso ; Alexandre Miguel Pinto ; Francesco Guerra ; Geert-Jan Houben. Springer Verlag, 2015. pp. 64-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Classification using various machine learning methods and combinations of key-phrases and visual features",

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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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 Y Velegrakis, J Cardoso, J Cardoso, AM Pinto, F Guerra & G-J Houben (eds), Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9398, Springer Verlag, pp. 64-75, 1st COST Action IC1302 International KEYSTONE Conference on Semantic Keyword-Based Search on Structured Data Sources, IKC 2015, Coimbra, Portugal, 8/09/15. https://doi.org/10.1007/978-3-319-27932-9_6

Classification using various machine learning methods and combinations of key-phrases and visual features. / Hacohen-Kerner, Yaakov; Sabag, Asaf; Liparas, Dimitris et al.
Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. ed. / Yannis Velegrakis; Jorge Cardoso; Jorge Cardoso; Alexandre Miguel Pinto; Francesco Guerra; Geert-Jan Houben. Springer Verlag, 2015. p. 64-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9398).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sabag, Asaf

AU - Liparas, Dimitris

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AU - Vrochidis, Stefanos

AU - Kompatsiaris, Ioannis

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Hacohen-Kerner Y, Sabag A, Liparas D, Moumtzidou A, Vrochidis S, Kompatsiaris I. Classification using various machine learning methods and combinations of key-phrases and visual features. In Velegrakis Y, Cardoso J, Cardoso J, Pinto AM, Guerra F, Houben GJ, editors, Semantic Keyword-Based Search on Structured Data Sources First COST Action IC1302 – International KEYSTONE Conference, IKC 2015, Revised Selected Papers. Springer Verlag. 2015. p. 64-75. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-27932-9_6

Classification using various machine learning methods and combinations of key-phrases and visual features

Abstract

Publication series

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Bibliographical note

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Keywords

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