Multiple Features
This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Integer, Real
# Instances
2000
# Features
-
Dataset Information
Additional 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).
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
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Additional Variable 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.
Dataset Files
| File | Size |
|---|---|
| mfeat.tar | 8.5 MB |
| mfeat-fac | 2.1 MB |
| mfeat-kar | 1.8 MB |
| mfeat-fou | 1.7 MB |
| mfeat-pix | 1.4 MB |
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Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset multiple_features = fetch_ucirepo(id=72) # data (as pandas dataframes) X = multiple_features.data.features y = multiple_features.data.targets # metadata print(multiple_features.metadata) # variable information print(multiple_features.variables)
Duin, R. (1998). Multiple Features [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5HC70.
Creators
Robert Duin
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.