Data
autoUniv-au6-1000

autoUniv-au6-1000

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
0 likes downloaded by 16 people , 23 total downloads 0 issues 0 downvotes
  • artificial Chemistry Medicine study_34 study_50 study_7
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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
Show all 41 features

107 properties

NumberOfInstances
1000
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.
RandomTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.04
Number of attributes divided by the number of instances.
MaxMutualInformation
0.01
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
4.88
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
1.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1ErrRate
0.85
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
529.91
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
8
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.09
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.58
Third quartile of entropy among attributes.
RandomTreeDepth1Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
1.34
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.04
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
-0.39
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.13
Average class difference between consecutive instances.
RandomTreeDepth2AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
24536.42
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
8.9
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
90.24
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
76.9
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.53
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
RandomTreeDepth2ErrRate
0.85
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
1.19
Average entropy of the attributes.
MinorityClassSize
89
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
9.76
Percentage of nominal attributes.
Quartile3MutualInformation
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
0.78
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
0.03
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
RandomTreeDepth2Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
-0.79
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
1
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
0.38
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.53
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
RandomTreeDepth3AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
4239.05
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.83
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
-1.21
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
12.2
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesErrRate
0.78
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
RandomTreeDepth3ErrRate
0.85
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0.01
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
0.63
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
0.03
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
RandomTreeDepth3Kappa
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
215.67
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
2
Number of binary attributes.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
0.53
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
StdvNominalAttDistinctValues
2.87
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3.75
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-0.45
First quartile of skewness among attributes of the numeric type.
REPTreeDepth2AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
0.78
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
kNN1NAUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
-0.05
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.18
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2ErrRate
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
0.03
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
kNN1NErrRate
0.85
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
24
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
1354.84
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1
Second quartile (Median) of entropy among attributes.
REPTreeDepth2Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
2.92
Entropy of the target attribute values.
kNN1NKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
240
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
-1.04
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth3AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
1.58
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.46
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
7.12
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3ErrRate
0.75
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.71
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
0.7
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.3
Minimum 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.
REPTreeDepth3Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
52106.18
Maximum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0.17
Second quartile (Median) of skewness among attributes of the numeric type.
Show all 107 properties

47 tasks

Supervised Classification on autoUniv-au6-1000
550 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on autoUniv-au6-1000
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on autoUniv-au6-1000
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Learning Curve on autoUniv-au6-1000
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Clustering on autoUniv-au6-1000
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on autoUniv-au6-1000
1300 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1300 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1299 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1299 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1297 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1297 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1297 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
1297 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Subgroup Discovery on autoUniv-au6-1000
0 runs - target_feature: Class
Define a new task