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autoUniv-au6-400

autoUniv-au6-400

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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
V22numeric33 unique values
0 missing
V21numeric101 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
V11numeric59 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
V1numeric172 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
Show all 41 features

107 properties

NumberOfInstances
400
Number of instances (rows) of the dataset.
NumberOfFeatures
41
Number of attributes (columns) of the dataset.
NumberOfClasses
8
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
37
Number of numeric attributes.
NumberOfSymbolicFeatures
4
Number of nominal attributes.
AutoCorrelation
0.18
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
2.77
Entropy of the target attribute values.
DecisionStumpAUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.7
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.1
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
139.96
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.74
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
27.75
Percentage of instances belonging to the most frequent class.
MajorityClassSize
111
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.57
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.34
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
52300.41
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.04
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
8
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.35
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
24670.87
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.19
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.76
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
4183
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.02
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
59.09
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3.75
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.05
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1378.35
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.99
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.48
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.3
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.01
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.31
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.04
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
6.25
Percentage of instances belonging to the least frequent class.
MinorityClassSize
25
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.74
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
4.88
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
90.24
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
9.76
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.99
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.19
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.64
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.43
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.18
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
7.17
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.13
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.98
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.57
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.46
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
77.28
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.04
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.39
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
11.95
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.68
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2.87
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.83
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

13 tasks

Supervised Classification on autoUniv-au6-400
113 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on autoUniv-au6-400
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Clustering on autoUniv-au6-400
0 runs
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-400
0 runs - estimation_procedure: 50 times Clustering
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