Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Lung Cancer Data Set
Download: Data Folder, Data Set Description

Abstract: Lung cancer data; no attribute definitions

Data Set Characteristics:  

Multivariate

Number of Instances:

32

Area:

Life

Attribute Characteristics:

Integer

Number of Attributes:

56

Date Donated

1992-05-01

Associated Tasks:

Classification

Missing Values?

Yes

Number of Web Hits:

425114


Source:

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


Data Set 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.

Notes:
- 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). (*)


Attribute Information:

Attribute 1 is the class label.

All predictive attributes are nominal, taking on integer values 0-3


Relevant Papers:

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.
[Web Link]

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.


Papers That Cite This Data Set1:

Jinyan Li and Limsoon Wong. Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL. WAIM. 2003. [View Context].

Manoranjan Dash and Huan Liu. Hybrid Search of Feature Subsets. PRICAI. 1998. [View Context].

Glenn Fung and Sathyakama Sandilya and R. Bharat Rao. Rule extraction from Linear Support Vector Machines. Computer-Aided Diagnosis & Therapy, Siemens Medical Solutions, Inc. [View Context].


Citation Request:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML