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autoUniv-au6-400

autoUniv-au6-400

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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  • artificial Chemistry Life Science mf_less_than_80 study_123 study_50 study_52 study_7 study_88
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Author: Ray. J. Hickey Source: UCI Please cite: * Dataset Title: AutoUniv Dataset data problem: autoUniv-au6-cd1-400 * 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
V1numeric172 unique values
0 missing
V2numeric392 unique values
0 missing
V3numeric216 unique values
0 missing
V4numeric174 unique values
0 missing
V5numeric400 unique values
0 missing
V6numeric139 unique values
0 missing
V7numeric117 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
V13numeric168 unique values
0 missing
V14numeric226 unique values
0 missing
V15numeric379 unique values
0 missing
V16numeric71 unique values
0 missing
V17numeric221 unique values
0 missing
V18numeric209 unique values
0 missing
V19numeric58 unique values
0 missing
V20numeric209 unique values
0 missing
V21numeric101 unique values
0 missing
V22numeric33 unique values
0 missing
V23numeric246 unique values
0 missing
V24numeric230 unique values
0 missing
V25numeric139 unique values
0 missing
V26numeric304 unique values
0 missing
V27numeric194 unique values
0 missing
V28numeric60 unique values
0 missing
V29numeric110 unique values
0 missing
V30numeric42 unique values
0 missing
V31nominal2 unique values
0 missing
V32numeric134 unique values
0 missing
V33numeric30 unique values
0 missing
V34numeric251 unique values
0 missing
V35numeric161 unique values
0 missing
V36numeric71 unique values
0 missing
V37numeric400 unique values
0 missing
V38numeric177 unique values
0 missing
V39numeric149 unique values
0 missing
V40nominal3 unique values
0 missing

107 properties

400
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.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
52300.41
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
-0.13
Second quartile (Median) of skewness among attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.1
Number of attributes divided by the number of instances.
0.04
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
4.88
Percentage of binary attributes.
0.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
139.96
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
-1.31
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
1.57
Third quartile of entropy among attributes.
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.35
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.46
Third quartile of kurtosis among attributes of the numeric type.
0.18
Average class difference between consecutive instances.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
24670.87
Maximum standard deviation of attributes of the numeric type.
6.25
Percentage of instances belonging to the least frequent class.
90.24
Percentage of numeric attributes.
77.28
Third quartile of means among attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.19
Average entropy of the attributes.
25
Number of instances belonging to the least frequent class.
9.76
Percentage of nominal attributes.
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
0.72
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.76
Mean kurtosis among attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.99
First quartile of entropy among attributes.
0.39
Third quartile of skewness among attributes of the numeric type.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4183
Mean of means among attributes of the numeric type.
0.74
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.19
First quartile of kurtosis among attributes of the numeric type.
11.95
Third quartile of standard deviation of attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.02
Average mutual information between the nominal attributes and the target attribute.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.64
First quartile of means among attributes of the numeric type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
59.09
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
2.87
Standard deviation of the number of distinct values among attributes of the nominal type.
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.75
Average number of distinct values among the attributes of the nominal type.
-0.43
First quartile of skewness among attributes of the numeric type.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.72
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0.05
Mean skewness among attributes of the numeric type.
0.18
First quartile of standard deviation of attributes of the numeric type.
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.83
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
27.75
Percentage of instances belonging to the most frequent class.
1378.35
Mean standard deviation of attributes of the numeric type.
1
Second quartile (Median) of entropy among attributes.
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.77
Entropy of the target attribute values.
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
111
Number of instances belonging to the most frequent class.
0.99
Minimal entropy among attributes.
-1.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.57
Maximum entropy among attributes.
-1.48
Minimum kurtosis among attributes of the numeric type.
7.17
Second quartile (Median) of means among attributes of the numeric type.
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.7
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.34
Maximum kurtosis among attributes of the numeric type.
0.3
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

13 tasks

113 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: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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