Commit Inicial

data/tanveer/lung-cancer/lung-cancer.names

@@ -0,0 +1,61 @@
+
+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     "
+