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autoUniv-au4-2500

autoUniv-au4-2500

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-au4-2500 * 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.

101 features

Class (target)nominal3 unique values
0 missing
V1numeric2 unique values
0 missing
V2numeric5 unique values
0 missing
V3nominal6 unique values
0 missing
V4nominal4 unique values
0 missing
V5numeric46 unique values
0 missing
V6nominal3 unique values
0 missing
V7numeric2 unique values
0 missing
V8numeric4 unique values
0 missing
V9nominal4 unique values
0 missing
V10numeric4 unique values
0 missing
V11numeric2014 unique values
0 missing
V12nominal3 unique values
0 missing
V13nominal2 unique values
0 missing
V14nominal5 unique values
0 missing
V15nominal6 unique values
0 missing
V16numeric49 unique values
0 missing
V17numeric599 unique values
0 missing
V18numeric129 unique values
0 missing
V19nominal2 unique values
0 missing
V20numeric40 unique values
0 missing
V21numeric190 unique values
0 missing
V22numeric157 unique values
0 missing
V23numeric102 unique values
0 missing
V24numeric5 unique values
0 missing
V25nominal4 unique values
0 missing
V26numeric2 unique values
0 missing
V27numeric209 unique values
0 missing
V28numeric5 unique values
0 missing
V29numeric35 unique values
0 missing
V30numeric330 unique values
0 missing
V31nominal2 unique values
0 missing
V32numeric238 unique values
0 missing
V33numeric69 unique values
0 missing
V34nominal6 unique values
0 missing
V35numeric1174 unique values
0 missing
V36numeric725 unique values
0 missing
V37nominal5 unique values
0 missing
V38nominal4 unique values
0 missing
V39nominal5 unique values
0 missing
V40nominal2 unique values
0 missing
V41numeric4 unique values
0 missing
V42numeric980 unique values
0 missing
V43numeric5 unique values
0 missing
V44nominal5 unique values
0 missing
V45numeric159 unique values
0 missing
V46numeric4 unique values
0 missing
V47nominal6 unique values
0 missing
V48nominal3 unique values
0 missing
V49nominal6 unique values
0 missing
V50numeric3 unique values
0 missing
V51nominal5 unique values
0 missing
V52numeric4 unique values
0 missing
V53numeric365 unique values
0 missing
V54numeric2 unique values
0 missing
V55nominal5 unique values
0 missing
V56nominal5 unique values
0 missing
V57nominal3 unique values
0 missing
V58nominal3 unique values
0 missing
V59numeric2342 unique values
0 missing
V60numeric1056 unique values
0 missing
V61numeric640 unique values
0 missing
V62numeric2 unique values
0 missing
V63numeric299 unique values
0 missing
V64nominal5 unique values
0 missing
V65numeric3 unique values
0 missing
V66nominal2 unique values
0 missing
V67numeric1911 unique values
0 missing
V68nominal5 unique values
0 missing
V69nominal4 unique values
0 missing
V70numeric182 unique values
0 missing
V71numeric4 unique values
0 missing
V72numeric1095 unique values
0 missing
V73numeric60 unique values
0 missing
V74numeric1837 unique values
0 missing
V75numeric3 unique values
0 missing
V76numeric32 unique values
0 missing
V77numeric56 unique values
0 missing
V78numeric6 unique values
0 missing
V79nominal5 unique values
0 missing
V80numeric2 unique values
0 missing
V81nominal4 unique values
0 missing
V82numeric348 unique values
0 missing
V83nominal3 unique values
0 missing
V84nominal2 unique values
0 missing
V85numeric209 unique values
0 missing
V86numeric449 unique values
0 missing
V87numeric4 unique values
0 missing
V88numeric450 unique values
0 missing
V89nominal6 unique values
0 missing
V90nominal3 unique values
0 missing
V91numeric3 unique values
0 missing
V92nominal2 unique values
0 missing
V93nominal2 unique values
0 missing
V94numeric2 unique values
0 missing
V95numeric934 unique values
0 missing
V96nominal5 unique values
0 missing
V97nominal3 unique values
0 missing
V98nominal6 unique values
0 missing
V99nominal2 unique values
0 missing
V100nominal5 unique values
0 missing

62 properties

2500
Number of instances (rows) of the dataset.
101
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.
58
Number of numeric attributes.
43
Number of nominal attributes.
8.91
Percentage of binary attributes.
1.3
Second quartile (Median) of standard deviation of attributes of the numeric type.
2.58
Maximum entropy among attributes.
-2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
2.32
Third quartile of entropy among attributes.
0.36
Maximum kurtosis among attributes of the numeric type.
0.26
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.9
Third quartile of kurtosis among attributes of the numeric type.
57622.81
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
57.43
Percentage of numeric attributes.
280.96
Third quartile of means among attributes of the numeric type.
0.06
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
42.57
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-0.65
Minimum skewness among attributes of the numeric type.
1.57
First quartile of entropy among attributes.
0.26
Third quartile of skewness among attributes of the numeric type.
1.28
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
-1.44
First quartile of kurtosis among attributes of the numeric type.
25.69
Third quartile of standard deviation of attributes of the numeric type.
20654.21
Maximum standard deviation of attributes of the numeric type.
7.84
Percentage of instances belonging to the least frequent class.
0.96
First quartile of means among attributes of the numeric type.
1.42
Standard deviation of the number of distinct values among attributes of the nominal type.
1.89
Average entropy of the attributes.
196
Number of instances belonging to the least frequent class.
0
First quartile of mutual information between the nominal attributes and the target attribute.
-1.14
Mean kurtosis among attributes of the numeric type.
9
Number of binary attributes.
-0.12
First quartile of skewness among attributes of the numeric type.
2902.26
Mean of means among attributes of the numeric type.
0.5
First quartile of standard deviation of attributes of the numeric type.
0.44
Average class difference between consecutive instances.
0
Average mutual information between the nominal attributes and the target attribute.
2
Second quartile (Median) of entropy among attributes.
1.32
Entropy of the target attribute values.
520.72
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.24
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.04
Number of attributes divided by the number of instances.
3.98
Average number of distinct values among the attributes of the nominal type.
2.29
Second quartile (Median) of means among attributes of the numeric type.
363.95
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.07
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
46.92
Percentage of instances belonging to the most frequent class.
625.32
Mean standard deviation of attributes of the numeric type.
0.01
Second quartile (Median) of skewness among attributes of the numeric type.
1173
Number of instances belonging to the most frequent class.
0.99
Minimal entropy among attributes.

19 tasks

253 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 - evaluation_measure: predictive_accuracy - 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
1298 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
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