KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

KEEL-dataset - data set description

Lymphography data set

Description
Files and additional references

Description

This section describes main characteristics of the lymphography data set and its attributes:

General information

Lymphography data set
Type	Classification	Origin	Real world
Features	18	(Real / Integer / Nominal)	(0 / 3 / 15)
Instances	148	Classes	4
Missing values?			No

Attribute description

Attribute	Domain	Attribute	Domain
Lymphatics	{normal,arched,deformed,displaced}	Lym_nodes_enlar	[1,4]
Block_of_affere	{no,yes}	Changes_in_lym	{bean,oval,round}
Bl_of_lymph_c	{no,yes}	Defect_in_node	{no,lacunar,lac_margin,lac_central}
Bl_of_lymph_s	{no,yes}	Changes_in_node	{no,lacunar,lac_margin,lac_central}
By_pass	{no,yes}	Changes_in_stru	{no,grainy,drop_like,coarse,diluted,reticular,stripped,faint}
Extravasates	{no,yes}	Special_forms	{no,chalices,vesicles}
Regeneration_of	{no,yes}	Dislocation_of	{no,yes}
Early_uptake_in	{no,yes}	Exclusion_of_no	{no,yes}
Lym_nodes_dimin	[0,3]	No_of_nodes_in	[1,8]
Class	{normal,metastases,malign_lymph,fibrosis}

Additional information

This is a domain provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. The task is to detect the presence of a lymphoma and its current status.

Files and additional references

In this section you can download some files related to the lymphography data set:

The complete data set already formatted in KEEL format can be downloaded from here .
A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here .
A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here .
The header file associated to this data set can be downloaded from here .
This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository . The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Lymphography.