# Info on Lung Cancer Data Base

    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     "