View datasets-numeric meta (public)

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Summary

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License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Integer,Floating Point,String
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# Instances: 528 / # Attributes: 22
HDF5 (132.0 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
'meta'
Version mldata
0
Comment
  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:

  8. DS_Name categorical Name of DataSet

  9. T continuous Number of examples in test set

  10. N continuous Number of examples

  11. p continuous Number of attributes

  12. k continuous Number of classes

  13. Bin continuous Number of binary Attributes

  14. Cost continuous Cost (1=yes,0=no)

  15. SDratio continuous Standard deviation ratio

  16. correl continuous Mean correlation between attributes

  17. cancor1 continuous First canonical correlation

  18. cancor2 continuous Second canonical correlation

  19. fract1 continuous First eigenvalue

  20. fract2 continuous Second eigenvalue

  21. skewness continuous Mean of |E(X-Mean)|^3/STD^3

  22. kurtosis continuous Mean of |E(X-Mean)|^4/STD^4

  23. Hc continuous Mean entropy of attributes

  24. Hx continuous Entropy of classes

  25. MCx continuous Mean mutual entropy of class and attributes

  26. EnAtr continuous Equivalent number of attributes

  27. NSRatio continuous Noise-signal ratio

  28. Alg_Name categorical Name of Algorithm

  29. Norm_error continuous Normalized Error (continuous class)

  30. 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
Names
DS_Name,T,N,p,k,Bin,Cost,SDratio,correl,cancor1,
Types
  1. nominal:Aust_Credit,BT,Belgian,CUT,Chromosone,Credit,DNA,Diabetes,Digits,Faults,German_Credit,Head,Heart,KlDigits,Letters,NewBelgian,SatImage,Segment,Shuttle,Technical,TseTse,Vehicle
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
  8. numeric
  9. numeric
  10. numeric
Data (first 10 data points)
    DS_N... T N p k Bin Cost SDra... correl canc... ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    Aust... 690 690 14 2 4 0 1.2623 0.1024 0.7713 ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

A jarfile containing 37 regression problems, obtained from various sources (datasets-numeric.jar, 169,344 Bytes).

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    revision 1
    by mldata on 2011-09-14 16:26

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