Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,Floating Point

Download

# Instances: 32 / # Attributes: 57
HDF5 (21.1 KB) XML CSV ARFF LibSVM Matlab Octave

Original Data Format

arff

Name

lung-cancer

Version mldata

0

Comment

1. Title: Lung Cancer Data

2. Source Information:

   • Data was published in : Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane", Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991.
   • Donor: Stefan Aeberhard, stefan@coral.cs.jcu.edu.au
   • Date : May, 1992

3. Past Usage:

   • Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane", Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991.
   • Aeberhard, S., Coomans, D, De Vel, O. "Comparisons of Classification Methods in High Dimensional Settings", submitted to Technometrics.
   • Aeberhard, S., Coomans, D, De Vel, O. "The Dangers of Bias in High Dimensional Settings", submitted to pattern Recognition.

4. Relevant Information:

   • This data was used by Hong and Young to illustrate the power of the optimal discriminant plane even in ill-posed settings. Applying the KNN method in the resulting plane
     gave 77% accuracy. However, these results are strongly biased (See Aeberhard's second ref. above, or email to stefan@coral.cs.jcu.edu.au). Results obtained by Aeberhard et al. are : RDA : 62.5%, KNN 53.1%, Opt. Disc. Plane 59.4%

   The data described 3 types of pathological lung cancers. The Authors give no information on the individual variables nor on where the data was originally used.

   • In the original data 4 values for the fifth attribute were -1. These values have been changed to ? (unknown). (*)
   • In the original data 1 value for the 39 attribute was 4. This value has been changed to ? (unknown). (*)

5. Number of Instances: 32

6. Number of Attributes: 57 (1 class attribute, 56 predictive)

7. Attribute Information:

   attribute 1 is the class label.

   • All predictive attributes are nominal, taking on integer values 0-3

8. Missing Attribute Values: Attributes 5 and 39 (*)

9. Class Distribution:

   • 3 classes, 1.) 9 observations 2.) 13 " 3.) 10 "

Information about the dataset CLASSTYPE: nominal CLASSINDEX: first

Names

class,attribute2,attribute3,attribute4,attribute5,attribute6,attribute7,attribute8,attribute9,attribute10,

Types

1. nominal:1,2,3
2. nominal:0,1
3. nominal:1,2,3
4. nominal:0,1,2,3
5. nominal:0,1,2
6. nominal:0,1
7. nominal:1,2,3
8. nominal:1,2,3
9. nominal:1,2,3
10. nominal:1,2,3

Data (first 10 data points)

class	attr...	attr...	attr...	attr...	attr...	attr...	attr...	attr...	attr...	...
1	0	3	0	nan	0	2	2	2	1	...
1	0	3	3	1.0	0	3	1	3	1	...
1	0	3	3	2.0	0	3	3	3	1	...
1	0	2	3	2.0	1	3	3	3	1	...
1	0	3	2	1.0	1	3	3	3	2	...
1	0	3	3	2.0	0	3	3	3	1	...
1	0	3	2	1.0	0	3	3	3	1	...
1	0	2	2	1.0	0	3	1	3	3	...
1	0	3	1	1.0	0	3	1	3	1	...
2	0	2	3	2.0	0	2	2	2	1	...
...	...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

URLs

(No information yet)

Publications

Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 09:58

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