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Author: Source: Unknown - Date unknown Please cite: 1. Title: meta-data 2. Sources: (a) Creator: LIACC - University of Porto R.Campo Alegre 823 4150 PORTO (b) Donor: P.B.Brazdil or J.Gama Tel.: +351 600 1672 LIACC, University of Porto Fax.: +351 600 3654 Rua Campo Alegre 823 Email: statlog-adm@ncc.up.pt 4150 Porto, Portugal (c) Date: March, 1996 (d) 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 3. Past Usage: Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset. This work is described in: -"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 4. Relevant Information:n 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. Algorithms DataSets ------------------------- -------------------------- C4.5 NewId Credit_Austr Belgian AC2 CART Chromosome Credit_Man IndCART Cal5 CUT DNA CN2 ITRule Diabetes Digits44 Discrim QuaDisc Credit_German Faults LogDisc ALLOC80 Head Heart kNN SMART KLDigits Letters BayesTree CASTLE New_Belgian Sat_Image DIPLO92 RBF Segment Shuttle LVQ Backprop Technical TseTse Kohonen Vehicle The results of these tests are comprehensively described in a book (D.Michie et.al, 1994). 5. Number of Instances: 528 6. Number of Attributes: 22 (including an Id#) plus the class attribute -- all but two attributes are continuously valued 7. Attribute Information: 1. DS_Name categorical Name of DataSet 2. T continuous Number of examples in test set 3. N continuous Number of examples 4. p continuous Number of attributes 5. k continuous Number of classes 6. Bin continuous Number of binary Attributes 7. Cost continuous Cost (1=yes,0=no) 8. SDratio continuous Standard deviation ratio 9. correl continuous Mean correlation between attributes 10. cancor1 continuous First canonical correlation 11. cancor2 continuous Second canonical correlation 12. fract1 continuous First eigenvalue 13. fract2 continuous Second eigenvalue 14. skewness continuous Mean of |E(X-Mean)|^3/STD^3 15. kurtosis continuous Mean of |E(X-Mean)|^4/STD^4 16. Hc continuous Mean entropy of attributes 17. Hx continuous Entropy of classes 18. MCx continuous Mean mutual entropy of class and attributes 19. EnAtr continuous Equivalent number of attributes 20. NSRatio continuous Noise-signal ratio 21. Alg_Name categorical Name of Algorithm 22. Norm_error continuous Normalized Error (continuous class) 8. Missing Attribute Values: Note that fract2 and cancor2 only apply to datasets with more than 2 classes. When they appear as '?' this means a don't care value. Summary Statistics: Attribute Min Max Mean Std T 270 20000 4569.05 5704.01 N 270 58000 10734.2 14568.8 p 6 180 29.5455 36.8533 k 2 91 9.72727 19.3568 Bin 0 43 3.18182 9.29227 Cost 0 1 0.13636 0.35125 SdRatio 1.0273 4.0014 1.4791 0.65827 Correl 0.0456 0.751 0.23684 0.1861 Cancor1 0.5044 0.9884 0.79484 0.15639 Cancor2 0.1057 0.9623 0.74106 0.269 Fract1 0.1505 1 0.70067 0.3454 Fract2 0.2807 1 0.70004 0.29405 Skewness 0.1802 6.7156 1.78422 1.79022 Kurtosis 0.9866 160.311 22.6672 41.8496 Hc 0.2893 4.8787 1.87158 1.44665 Hx 0.3672 6.5452 3.34502 1.80383 Mcx 0.0187 1.3149 0.31681 0.33548 EnAtr 1.56006 160.644 20.6641 35.6614 NsRatio 1.02314 159.644 28.873 37.925