AutoUniv
Donated on 11/2/2010
AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats.
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Categorical, Integer, Real
# Instances
-
# Features
-
Dataset Information
Additional Information
The user first creates a classification model and then generates classified examples from it. To create a model, the following are specified: the number of attributes (up to 1000) and their type (discrete or continuous), the number of classes (up to 10), the complexity of the underlying rules and the noise level. AutoUniv then produces a model through a process of constrained randomised search to satisfy the user's requirements. A model can have up to 3000 rules. Rare class models can be designed. A sequence of models can be designed to reflect concept and/or population drift. AutoUniv creates three text files for a model: a Prolog specification of the model used to generate examples (.aupl); a user-friendly statement of the classification rules in an 'if ... then' format (.aurules); a statistical summary of the main properties of the model, including its Bayes rate (.auprops).
Has Missing Values?
No
Variable Information
Attributes may be discrete with up to 10 values or continuous. A discrete attribute can be nominal with values v1, v2, v3 ... or integer with values 0, 1, 2 , ... .
Dataset Files
| File | Size |
|---|---|
| AU/Models/au8.aupl | 12.5 MB |
| AU/Data Sets/au2_10000.csv | 10.7 MB |
| AU/Data Sets/au3_25000.data | 2.3 MB |
| AU/Data Sets/au8_500.data | 1.5 MB |
| AU/Models/au2.aupl | 1.4 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset autouniv = fetch_ucirepo(id=197) # data (as pandas dataframes) X = autouniv.data.features y = autouniv.data.targets # metadata print(autouniv.metadata) # variable information print(autouniv.variables)
Hickey, R. (2010). AutoUniv [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5690V.
Creators
Ray. Hickey
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.