| Converted by Ronny Kohavi from Irvine
| First attribute moved to the end in the data file.
|
| 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     "
| 
1, 2, 3.

A00: 0,1,2,3.
A01: 0,1,2,3.
A02: 0,1,2,3.
A03: 0,1,2,3.
A04: 0,1,2,3.
A05: 0,1,2,3.
A06: 0,1,2,3.
A07: 0,1,2,3.
A08: 0,1,2,3.
A09: 0,1,2,3.
A10: 0,1,2,3.
A11: 0,1,2,3.
A12: 0,1,2,3.
A13: 0,1,2,3.
A14: 0,1,2,3.
A15: 0,1,2,3.
A16: 0,1,2,3.
A17: 0,1,2,3.
A18: 0,1,2,3.
A19: 0,1,2,3.
A20: 0,1,2,3.
A21: 0,1,2,3.
A22: 0,1,2,3.
A23: 0,1,2,3.
A24: 0,1,2,3.
A25: 0,1,2,3.
A26: 0,1,2,3.
A27: 0,1,2,3.
A28: 0,1,2,3.
A29: 0,1,2,3.
A30: 0,1,2,3.
A31: 0,1,2,3.
A32: 0,1,2,3.
A33: 0,1,2,3.
A34: 0,1,2,3.
A35: 0,1,2,3.
A36: 0,1,2,3.
A37: 0,1,2,3.
A38: 0,1,2,3.
A39: 0,1,2,3.
A40: 0,1,2,3.
A41: 0,1,2,3.
A42: 0,1,2,3.
A43: 0,1,2,3.
A44: 0,1,2,3.
A45: 0,1,2,3.
A46: 0,1,2,3.
A47: 0,1,2,3.
A48: 0,1,2,3.
A49: 0,1,2,3.
A50: 0,1,2,3.
A51: 0,1,2,3.
A52: 0,1,2,3.
A53: 0,1,2,3.
A54: 0,1,2,3.
A55: 0,1,2,3.