openml-datasets

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Data Files

File	Description	Size	Last modified	Download
lung-cancer		7.85 kB	3 months ago	lung-cancer
lung-cancer		4.31 kB	3 months ago	lung-cancer

Files	Size	Format	Created	Updated	License	Source
2	12.2 kB	arff, csv		about 2 months ago	Open Data Commons Public Domain Dedication and License

The resources for this dataset can be found at https://www.openml.org/d/163

Author:
Source: Unknown -
Please cite:

Title: Lung Cancer Data
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
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.
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). (*)
Number of Instances: 32
Number of Attributes: 57 (1 class attribute, 56 predictive)
Attribute Information:

attribute 1 is the class label.
- All predictive attributes are nominal, taking on integer values 0-3
Missing Attribute Values: Attributes 5 and 39 (*)
Class Distribution:
- 3 classes, 1.) 9 observations 2.) 13 " 3.) 10 "

Information about the dataset CLASSTYPE: nominal CLASSINDEX: first