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

Nursery data set

Description
Files and additional references

Description

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

General information

Nursery data set
Type	Classification	Origin	Real world
Features	8	(Real / Integer / Nominal)	(0 / 0 / 8)
Instances	12690	Classes	5
Missing values?			No

Attribute description

Attribute	Domain
Parents	{pretentious, great_pret, usual}
Has_nurs	{critical, very_crit, less_proper, proper, improper}
Form	{incomplete, foster, completed, complete}
Children	{1, 3, more, 2}
Housing	{critical, less_conv, convenient}
Finance	{inconv, convenient}
Social	{problematic, nonprob, slightly_prob}
Health	{priority, not_recom, recommended}
Class	{recommend, very_recom, spec_prior, priority, not_recom}

Additional information

This database was derived from a hierarchical decision model originally developed to rank applications for nursery schools. It was used during several years in 1980\'s when there was excessive enrollment to these schools in Ljubljana, Slovenia, and the rejected applications frequently needed an objective explanation. The task is to rank applications for a given nursery school.

Files and additional references

In this section you can download some files related to the nursery 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/Nursery.