A fuzzy model of a European index based on automatically extracted content information

D.V. Milea; R.J. Almeida; U. Kaymak; F. Frasincar

doi:10.1109/CIFER.2011.5953571

A fuzzy model of a European index based on automatically extracted content information

D.V. Milea, R.J. Almeida, U. Kaymak, F. Frasincar

Information Systems IE&IS

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

Samenvatting

In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.

Originele taal-2	Engels
Titel	Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France
Plaats van productie	Piscataway, USA
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	140-147
ISBN van geprinte versie	978-1-4244-9934-2
DOI's	https://doi.org/10.1109/CIFER.2011.5953571
Status	Gepubliceerd - 2011
Evenement	conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15 - Duur: 11 apr. 2011 → 15 apr. 2011

Congres

Congres	conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15
Periode	11/04/11 → 15/04/11
Ander	2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011)

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10.1109/CIFER.2011.5953571

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Milea, D. V., Almeida, R. J., Kaymak, U., & Frasincar, F. (2011). A fuzzy model of a European index based on automatically extracted content information. In Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France (blz. 140-147). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CIFER.2011.5953571

Milea, D.V. ; Almeida, R.J. ; Kaymak, U. et al. / A fuzzy model of a European index based on automatically extracted content information. Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France. Piscataway, USA : Institute of Electrical and Electronics Engineers, 2011. blz. 140-147

@inproceedings{5c245b5a138c40698ba55309acc9707a,

title = "A fuzzy model of a European index based on automatically extracted content information",

abstract = "In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.",

author = "D.V. Milea and R.J. Almeida and U. Kaymak and F. Frasincar",

year = "2011",

doi = "10.1109/CIFER.2011.5953571",

language = "English",

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booktitle = "Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15 ; Conference date: 11-04-2011 Through 15-04-2011",

}

Milea, DV, Almeida, RJ, Kaymak, U & Frasincar, F 2011, A fuzzy model of a European index based on automatically extracted content information. in Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France. Institute of Electrical and Electronics Engineers, Piscataway, USA, blz. 140-147, conference; 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011); 2011-04-11; 2011-04-15, 11/04/11. https://doi.org/10.1109/CIFER.2011.5953571

A fuzzy model of a European index based on automatically extracted content information. / Milea, D.V.; Almeida, R.J.; Kaymak, U. et al.
Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France. Piscataway, USA: Institute of Electrical and Electronics Engineers, 2011. blz. 140-147.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Kaymak, U.

AU - Frasincar, F.

PY - 2011

Y1 - 2011

N2 - In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.

AB - In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.

U2 - 10.1109/CIFER.2011.5953571

DO - 10.1109/CIFER.2011.5953571

M3 - Conference contribution

SN - 978-1-4244-9934-2

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BT - Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France

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Milea DV, Almeida RJ, Kaymak U, Frasincar F. A fuzzy model of a European index based on automatically extracted content information. In Proceedings of the 2011 IEEE Symposium on Computational Intelligence for Financial Engineering & Economics (CIFEr 2011), April 11-15, 2011, Paris, France. Piscataway, USA: Institute of Electrical and Electronics Engineers. 2011. blz. 140-147 doi: 10.1109/CIFER.2011.5953571

A fuzzy model of a European index based on automatically extracted content information

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