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Multiple Features Data Set
Download: Data Folder, Data Set Description
Abstract: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
2000 |
Area: |
Computer |
Attribute Characteristics: |
Integer, Real |
Number of Attributes: |
649 |
Date Donated |
N/A |
Associated Tasks: |
Classification |
Missing Values? |
No |
Number of Web Hits: |
147776 |
Source:
Robert P.W. Duin
Department of Applied Physics
Delft University of Technology
P.O. Box 5046, 2600 GA Delft
The Netherlands
email: duin '@' ph.tn.tudelft.nl
http : //www.ph.tn.tudelft.nl/~duin
tel +31 15 2786143
Data Set Information:
This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images. These digits are represented in terms of the following six feature sets (files):
1. mfeat-fou: 76 Fourier coefficients of the character shapes;
2. mfeat-fac: 216 profile correlations;
3. mfeat-kar: 64 Karhunen-Love coefficients;
4. mfeat-pix: 240 pixel averages in 2 x 3 windows;
5. mfeat-zer: 47 Zernike moments;
6. mfeat-mor: 6 morphological features.
In each file the 2000 patterns are stored in ASCI on 2000 lines. The first 200 patterns are of class `0', followed by sets of 200 patterns for each of the classes `1' - `9'. Corresponding patterns in different feature sets (files) correspond to the same original character.
The source image dataset is lost. Using the pixel-dataset (mfeat-pix) sampled versions of the original images may be obtained (15 x 16 pixels).
Attribute Information:
6 Files:
1. mfeat-fou: 76 Fourier coefficients of the character shapes;
2. mfeat-fac: 216 profile correlations;
3. mfeat-kar: 64 Karhunen-Love coefficients;
4. mfeat-pix: 240 pixel averages in 2 x 3 windows;
5. mfeat-zer: 47 Zernike moments;
6. mfeat-mor: 6 morphological features.
Relevant Papers:
M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386.
[Web Link]
M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20),
A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, in preparation
Papers That Cite This Data Set1:
 Xiaoli Z. Fern and Carla Brodley. Cluster Ensembles for High Dimensional Clustering: An Empirical Study. Journal of Machine Learning Research n, a. 2004. [View Context].
Jaakko Peltonen and Samuel Kaski. Discriminative Components of Data. IEEE. 2004. [View Context].
Xiaofeng He and Partha Niyogi. Locality Preserving Projections. NIPS. 2003. [View Context].
Simon Perkins and James Theiler. Online Feature Selection using Grafting. ICML. 2003. [View Context].
Pavel Paclik and Robert P W Duin and Geert M. P. van Kempen and Reinhard Kohlus. On Feature Selection with Measurement Cost and Grouped Features. Pattern Recognition Group, Delft University of Technology. [View Context].
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