Data
autoUniv-au7-700

autoUniv-au7-700

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-au7-700 * 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)nominal3 unique values
0 missing
V1numeric356 unique values
0 missing
V2numeric274 unique values
0 missing
V3numeric55 unique values
0 missing
V4numeric3 unique values
0 missing
V5nominal2 unique values
0 missing
V6nominal2 unique values
0 missing
V7numeric571 unique values
0 missing
V8nominal3 unique values
0 missing
V9numeric3 unique values
0 missing
V10nominal3 unique values
0 missing
V11numeric147 unique values
0 missing
V12numeric3 unique values
0 missing

62 properties

700
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
3
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.
245
Number of instances belonging to the most frequent class.
0.72
Minimal entropy among attributes.
-0.05
Second quartile (Median) of skewness among attributes of the numeric type.
1.58
Maximum entropy among attributes.
-1.53
Minimum kurtosis among attributes of the numeric type.
15.38
Percentage of binary attributes.
0.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
-1.08
Maximum kurtosis among attributes of the numeric type.
0.4
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
1.44
Third quartile of entropy among attributes.
5157.93
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
-1.18
Third quartile of kurtosis among attributes of the numeric type.
0.01
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
61.54
Percentage of numeric attributes.
2446.68
Third quartile of means among attributes of the numeric type.
3
The maximum number of distinct values among attributes of the nominal type.
-0.42
Minimum skewness among attributes of the numeric type.
38.46
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
0.73
Maximum skewness among attributes of the numeric type.
0.17
Minimum standard deviation of attributes of the numeric type.
0.79
First quartile of entropy among attributes.
0.27
Third quartile of skewness among attributes of the numeric type.
1429.64
Maximum standard deviation of attributes of the numeric type.
30.57
Percentage of instances belonging to the least frequent class.
-1.43
First quartile of kurtosis among attributes of the numeric type.
131.33
Third quartile of standard deviation of attributes of the numeric type.
1.08
Average entropy of the attributes.
214
Number of instances belonging to the least frequent class.
0.96
First quartile of means among attributes of the numeric type.
0.55
Standard deviation of the number of distinct values among attributes of the nominal type.
-1.31
Mean kurtosis among attributes of the numeric type.
2
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
1053.52
Mean of means among attributes of the numeric type.
-0.3
First quartile of skewness among attributes of the numeric type.
0.34
Average class difference between consecutive instances.
0
Average mutual information between the nominal attributes and the target attribute.
0.56
First quartile of standard deviation of attributes of the numeric type.
1.58
Entropy of the target attribute values.
227.43
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Second quartile (Median) of entropy among attributes.
0.02
Number of attributes divided by the number of instances.
2.6
Average number of distinct values among the attributes of the nominal type.
-1.32
Second quartile (Median) of kurtosis among attributes of the numeric type.
335.6
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.01
Mean skewness among attributes of the numeric type.
1.23
Second quartile (Median) of means among attributes of the numeric type.
35
Percentage of instances belonging to the most frequent class.
201.17
Mean standard deviation of attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

19 tasks

568 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1296 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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