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

MONK 2 data set

Description
Files and additional references

Description

This section describes main characteristics of the monk-2 data set and its attributes:

General information

MONK 2 data set
Type	Classification	Origin	Laboratory
Features	6	(Real / Integer / Nominal)	(0 / 6 / 0)
Instances	432	Classes	2
Missing values?			No

Attribute description

Attribute	Domain
A1	[1, 3]
A2	[1, 3]
A3	[1, 2]
A4	[1, 3]
A5	[1, 4]
A6	[1, 2]
Class	{0,1}

Additional information

The MONK's problems are a collection of three binary artificial classification problems (MONK-1, MONK-2 and MONK-3) over a six-attribute discrete domain. Each problem involves learning a binary function defined over this domain, from a sample of training examples that belong to class 0 or class 1.

This is the second problem of the collection, with no random noise added.

Files and additional references

In this section you can download some files related to the monk-2 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/MONK%27s+Problems.