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

General information

MONK 2 data set
TypeClassificationOriginLaboratory
Features 6(Real / Integer / Nominal)(0 / 6 / 0)
Instances432 Classes2
Missing values?No

Attribute description

AttributeDomain
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.




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 herezip.gif.
  • A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from herezip.gif.
  • A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from herezip.gif.
  • The header file associated to this data set can be downloaded from heretxt.png.
  • 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.


 
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