Data
autoUniv-au6-750

autoUniv-au6-750

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-au6-250-drift-au6-cd1-500 * 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.

41 features

Class (target)nominal8 unique values
0 missing
V1numeric241 unique values
0 missing
V2numeric722 unique values
0 missing
V3numeric315 unique values
0 missing
V4numeric251 unique values
0 missing
V5numeric742 unique values
0 missing
V6numeric199 unique values
0 missing
V7numeric136 unique values
0 missing
V8numeric44 unique values
0 missing
V9numeric48 unique values
0 missing
V10numeric62 unique values
0 missing
V11numeric58 unique values
0 missing
V12nominal2 unique values
0 missing
V13numeric213 unique values
0 missing
V14numeric325 unique values
0 missing
V15numeric676 unique values
0 missing
V16numeric71 unique values
0 missing
V17numeric346 unique values
0 missing
V18numeric263 unique values
0 missing
V19numeric61 unique values
0 missing
V20numeric307 unique values
0 missing
V21numeric108 unique values
0 missing
V22numeric33 unique values
0 missing
V23numeric320 unique values
0 missing
V24numeric296 unique values
0 missing
V25numeric168 unique values
0 missing
V26numeric452 unique values
0 missing
V27numeric256 unique values
0 missing
V28numeric60 unique values
0 missing
V29numeric124 unique values
0 missing
V30numeric42 unique values
0 missing
V31nominal2 unique values
0 missing
V32numeric151 unique values
0 missing
V33numeric30 unique values
0 missing
V34numeric364 unique values
0 missing
V35numeric179 unique values
0 missing
V36numeric72 unique values
0 missing
V37numeric745 unique values
0 missing
V38numeric204 unique values
0 missing
V39numeric165 unique values
0 missing
V40nominal3 unique values
0 missing

19 properties

750
Number of instances (rows) of the dataset.
41
Number of attributes (columns) of the dataset.
8
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.
37
Number of numeric attributes.
4
Number of nominal attributes.
4.88
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.17
Average class difference between consecutive instances.
0
Percentage of missing values.
0.05
Number of attributes divided by the number of instances.
90.24
Percentage of numeric attributes.
22
Percentage of instances belonging to the most frequent class.
9.76
Percentage of nominal attributes.
165
Number of instances belonging to the most frequent class.
7.6
Percentage of instances belonging to the least frequent class.
57
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

37 tasks

567 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
47 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
1301 runs - target_feature: Class
1300 runs - target_feature: Class
1298 runs - target_feature: Class
1298 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 runs - target_feature: Class
1297 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
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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