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

Pen-Based Recognition of Handwritten Digits data set

Description
Files and additional references

Description

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

General information

Pen-Based Recognition of Handwritten Digits data set
Type	Classification	Origin	Real world
Features	16	(Real / Integer / Nominal)	(0 / 16 / 0)
Instances	10992	Classes	10
Missing values?			No

Attribute description

Attribute	Domain	Attribute	Domain
At1	[0, 100]	At9	[0, 100]
At2	[0, 100]	At10	[0, 100]
At3	[0, 100]	At11	[0, 100]
At4	[0, 100]	At12	[0, 100]
At5	[0, 100]	At13	[0, 100]
At6	[0, 100]	At14	[0, 100]
At7	[0, 100]	At15	[0, 100]
At8	[0, 100]	At16	[0, 100]
Class	{0,1,2,3,4,5,6,7,8,9}

Additional information

A digit data base made by collecting 250 samples from 44 writers, using only (x, y) coordinate information represented as constant length feature vectors, which were resampled to 8 points per digit (therefore the data set contains 8 points x 2 coordinates = 16 attributes).

The class label represents the code of the digit written.

Files and additional references

In this section you can download some files related to the penbased 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/Pen-Based+Recognition+of+Handwritten+Digits.