| 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.