View sector_scale (public)

2010-11-01 11:46 by mldata | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

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License
unknown (from LibSVMTools repository)
Dependencies
Tags
libsvm LibSVMTools slurped
Attribute Types
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# Instances: 19238 / # Attributes: 55198
HDF5 (18.1 MB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
libsvm
Name
sector_scale
Version mldata
0
Comment

LibSVM

Names
Data (first 10 data points)
    2 0.00... 0.00... 3e-05 0.01... 0.00... 0.00... 0.00... 0.00... 0.00... ...
    3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    5 0.00... 0.00... 4e-05 0.0 0.00... 0.004 0.00... 0.01... 0.01... ...
    6 0.00... 0.00... 4e-05 0.00... 0.00... 0.00... 0.00... 0.0025 0.00... ...
    8 0.00... 0.00... 3e-05 0.0 0.00... 0.00... 0.00... 0.00... 0.00... ...
    9 0.00... 0.00... 0.00... 0.03... 0.00... 0.01... 0.01... 0.01... 0.00... ...
    11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ...
    12 0.0005 0.00... 3e-05 0.01... 0.00... 0.00... 0.00... 0.00... 0.0 ...
    14 0.00... 0.0009 8e-05 0.0 0.00... 0.00... 0.00... 0.0 0.0 ...
    15 0.00... 0.00... 5e-05 0.01... 0.00... 0.00... 0.0 0.00... 0.00... ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

Preprocessing:

The scaled data was used in our KDD 08 paper. For unknown reason we could now only generate something close to it. The sources are from this page. We select train-0.tc and test-0.tc from ecoc-svm-data.tar.gz. A 2/1 training/testing split gives training and testing sets below. They are in the original format instead of the libsvm format: in each row the 2nd value gives the class label and subsequent numbers give pairs of feature IDs and values. We then do a kind of tf-idf transformation: ln(1+tf)*log_2(#docs/#coll_freq_of_term) and normalize each instance unit length.

            [JR01b,SSK08a]
          # of classes: 105# of data:
        6,412
              / 3,207 (testing)
            # of features:
        55,197
              / 55,197 (testing)
URLs
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Publications
    Data Source
    ref.html#AM98a AM98a]
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-01 11:46

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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