Summary

(No information yet)

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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