Data
autoUniv-au6-1000

autoUniv-au6-1000

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-au6-1000 * 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
V1numeric252 unique values
0 missing
V2numeric941 unique values
0 missing
V3numeric332 unique values
0 missing
V4numeric261 unique values
0 missing
V5numeric989 unique values
0 missing
V6numeric226 unique values
0 missing
V7numeric136 unique values
0 missing
V8numeric44 unique values
0 missing
V9numeric48 unique values
0 missing
V10numeric62 unique values
0 missing
V11numeric59 unique values
0 missing
V12nominal2 unique values
0 missing
V13numeric268 unique values
0 missing
V14numeric383 unique values
0 missing
V15numeric875 unique values
0 missing
V16numeric71 unique values
0 missing
V17numeric409 unique values
0 missing
V18numeric297 unique values
0 missing
V19numeric61 unique values
0 missing
V20numeric348 unique values
0 missing
V21numeric109 unique values
0 missing
V22numeric33 unique values
0 missing
V23numeric347 unique values
0 missing
V24numeric320 unique values
0 missing
V25numeric186 unique values
0 missing
V26numeric526 unique values
0 missing
V27numeric266 unique values
0 missing
V28numeric60 unique values
0 missing
V29numeric145 unique values
0 missing
V30numeric42 unique values
0 missing
V31nominal2 unique values
0 missing
V32numeric150 unique values
0 missing
V33numeric30 unique values
0 missing
V34numeric418 unique values
0 missing
V35numeric179 unique values
0 missing
V36numeric72 unique values
0 missing
V37numeric994 unique values
0 missing
V38numeric221 unique values
0 missing
V39numeric169 unique values
0 missing
V40nominal3 unique values
0 missing

19 properties

1000
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.
2
Number of binary attributes.
4.88
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.13
Average class difference between consecutive instances.
90.24
Percentage of numeric attributes.
0.04
Number of attributes divided by the number of instances.
9.76
Percentage of nominal attributes.
24
Percentage of instances belonging to the most frequent class.
240
Number of instances belonging to the most frequent class.
8.9
Percentage of instances belonging to the least frequent class.
89
Number of instances belonging to the least frequent class.

38 tasks

566 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
47 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
1300 runs - target_feature: Class
1300 runs - target_feature: Class
1299 runs - target_feature: Class
1299 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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