meta: Meta

In tyluRp/ucimlr: UCI Machine Learning Repository

Description

This dataset is about the results of Statlog project. The project performed a comparative study between Statistical, Neural and Symbolic learning algorithms. Project StatLog (Esprit Project 5170) was concerned with comparative studies of different machine learning, neural and statistical classification algorithms. About 20 different algorithms were evaluated on more than 20 different datasets. The tests carried out under project produced many interesting results.

Usage

meta

Format

A data frame with 528 observations on the following 22 variables.

1. DS_Name

2. T

3. N

4. p

5. k

6. Bin

7. Cost

8. SDratio

9. correl

10. cancor1

11. cancor2

12. fract1

13. fract2

14. skewness

15. kurtosis

16. Hc

17. Hx

18. MCx

19. EnAtr

20. NSRatio

21. Alg_Name

22. Norm_error

Details

Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project).

Source

1. Creator: LIACC - University of Porto R.Campo Alegre 823 4150 PORTO

2. Donor: P.B.Brazdil or J.Gama Tel.: [phone] LIACC, University of Porto Fax.: [phone] Rua Campo Alegre 823 Email: [e-mail] 4150 Porto, Portugal

3. Date: March, 1996

Acknowlegements: LIACC wishes to thank Commission of European Communities for their support. Also, we wish to thank the following partners for providing the individual test results:

• Dept. of Statistics, University of Strathclyde, Glasgow, UK

• Dept. of Statistics, University of Leeds, UK

• Aston University, Birmingham, UK

• Forschungszentrum Ulm, Daimler-Benz AG, Germany

• Brainware GmbH, Berlin, Germany

• Frauenhofer Gesellschaft IITB-EPO, Berlin, Germany

• Institut fuer Kybernetik, Bochum, Germany

• ISoft, Gif sur Yvette, France

• Dept. of CS and AI, University of Granada, Spain

References

• "Machine Learning, Neural and Statistical Learning" Eds. D.Michie,D.J.Spiegelhalter and C.Taylor Ellis Horwood-1994

• "Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning", P. Brazdil, J.Gama and B.Henery: in Proc. of Machine Learning - ECML-94, ed. F.Bergadano and L.de Raedt,LNAI Vol.784 Springer-Verlag.

• "Characterization of Classification Algorithms" J.Gama, P.Brazdil in Proc. of EPIA 95, LNAI Vol.990 Springer-Verlag, 1995

https://archive.ics.uci.edu/ml/machine-learning-databases/meta-data/

https://archive.ics.uci.edu/ml/datasets/Meta-data

tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.