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autoUniv-au7-500

autoUniv-au7-500

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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Author: Ray. J. Hickey Source: UCI Please cite: * Dataset Title: AutoUniv Dataset data problem: autoUniv-au7-cpd1-500 * Abstract: 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. * Source: AutoUniv was developed by Ray. J. Hickey. Email: ray.j.hickey '@' gmail.com AutoUniv web-site: http://sites.google.com/site/autouniv/. * Data Set 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). * Attribute 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 , ... . * Relevant Papers: Marrs, G, Hickey, RJ and Black, MM (2010) Modeling the example life-cycle in an online classification learner. In Proceedings of HaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems. [Web Link]#proc . Marrs, G, Hickey, RJ and Black, MM (2010) The Impact of Latency on Online Classification Learning with Concept Drift. In Y. Bi and M.A. Williams (Eds.): KSEM 2010, LNAI 6291, Springer-Verlag, Berlin, pp. 459–469. Hickey, RJ (2007) Structure and Majority Classes in Decision Tree Learning. Journal of Machine Learning Research, 8, pp. 1747-1768.

13 features

Class (target)nominal5 unique values
0 missing
V1numeric270 unique values
0 missing
V2numeric236 unique values
0 missing
V3numeric55 unique values
0 missing
V4numeric3 unique values
0 missing
V5nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
V7numeric453 unique values
0 missing
V8nominal3 unique values
0 missing
V9numeric3 unique values
0 missing
V10nominal3 unique values
0 missing
V11numeric141 unique values
0 missing
V12numeric3 unique values
0 missing

19 properties

500
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
5
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
8
Number of numeric attributes.
5
Number of nominal attributes.
15.38
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.24
Average class difference between consecutive instances.
0
Percentage of missing values.
0.03
Number of attributes divided by the number of instances.
61.54
Percentage of numeric attributes.
38.4
Percentage of instances belonging to the most frequent class.
38.46
Percentage of nominal attributes.
192
Number of instances belonging to the most frequent class.
8.6
Percentage of instances belonging to the least frequent class.
43
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

25 tasks

590 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
46 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
1303 runs - target_feature: Class
1300 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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