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sponge

sponge

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). The multi-class target feature is converted to a two-class nominal target feature by re-labeling the majority class as positive ('P') and all others as negative ('N'). Originally converted by Quan Sun.

46 features

binaryClass (target)nominal2 unique values
0 missing
Name (ignore)nominal76 unique values
0 missing
A.CAPAS_DEL_CORTEXnominal4 unique values
0 missing
A.CAPA_INTERNA_DEL_CORTEXnominal6 unique values
0 missing
A.CORTEXnominal2 unique values
0 missing
A.CORTEX_FIBROSOnominal3 unique values
0 missing
A.CORTEX_SOLO_DE_ESPICULAS_TANGENCIALESnominal3 unique values
0 missing
A.CUERPOS_EXTRANOS_EN_EL_CORTEXnominal3 unique values
0 missing
A.GROSOR_DEL_CORTEXnominal5 unique values
0 missing
A.HACES_DE_ESPICULAS_PRINCIPALES_EN_POMPON_EN_EL_CORTEXnominal2 unique values
0 missing
A.TILOSTILOS_ADICIONALES_COANOSOMAnominal5 unique values
0 missing
B.NUMERO_DE_TIPOS_DE_MEGASCLERASnominal4 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_DIACTINA_TUBERCULADAnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_ESTILOnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_ESTILOS_2_TAMANOSnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_ESTILO_TILOSTILOnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_ESTRONGILOXAnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_OXASnominal2 unique values
0 missing
C.TIPO_ESPICULA_PRINCIPAL_TILOSTILOnominal2 unique values
0 missing
D.ESPICULA_PRINCIPAL_ESTILOnominal4 unique values
0 missing
D.ESPICULA_PRINCIPAL_TILOSTILOnominal4 unique values
0 missing
D.FORMA_BASE_TILOSTILO_PRINCIPALnominal4 unique values
0 missing
E.DISPOSICION_MEGASCLERAS_ECTOSOMICAS_EN_EL_ECTOSOMAnominal4 unique values
0 missing
E.FORMA_BASE_TILOSTILO_ECTOSOMICOnominal3 unique values
0 missing
E.FORMA_MEGASCLERA_ECTOSOMICAnominal4 unique values
0 missing
E.TIPO_MEGASCLERA_ECTOSOMICAnominal5 unique values
0 missing
F.TIPO_DE_EXOSTILOnominal6 unique values
0 missing
G.FORMA_MEGASCLERA_INTERMEDIARIAnominal4 unique values
0 missing
G.TIPO_MEGASCLERA_INTERMEDIARIAnominal4 unique values
0 missing
H.LONGITUD_MEGASCLERASnominal4 unique values
0 missing
I.MICROSCLERASnominal2 unique values
0 missing
I.TIPO_MICROSCLERAnominal5 unique values
0 missing
J.ASTERnominal2 unique values
0 missing
J.DIAMETRO_ESFERASTERnominal6 unique values
0 missing
J.TIPO_DE_ASTERnominal7 unique values
0 missing
J.TIPO_DE_DIPLASTERnominal3 unique values
0 missing
J.TIPO_DE_ESFERASTERnominal3 unique values
0 missing
K.FORMA_FINALnominal9 unique values
0 missing
L.NUMERO_DE_PAPILASnominal5 unique values
0 missing
L.PAPILASnominal2 unique values
0 missing
M.COLORnominal3 unique values
22 missing
N.SUPERFICIEnominal8 unique values
0 missing
O.DISPOSICION_ESPICULAR_ESQUELETOnominal7 unique values
0 missing
P.ALOJA_CANGREJO_ERMITANOnominal2 unique values
0 missing
P.PERFORANTEnominal2 unique values
0 missing
P.PSEUDORAICESnominal2 unique values
0 missing

107 properties

76
Number of instances (rows) of the dataset.
46
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
22
Number of missing values in the dataset.
22
Number of instances with at least one value missing.
0
Number of numeric attributes.
46
Number of nominal attributes.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.63
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.92
Average class difference between consecutive instances.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
7.89
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.5
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.12
Average entropy of the attributes.
6
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.05
Third quartile of mutual information between the nominal attributes and the target attribute.
0.08
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Mean kurtosis among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.53
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.05
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.5
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.04
Average mutual information between the nominal attributes and the target attribute.
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.08
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
27.21
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
16
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.71
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
1.8
Standard deviation of the number of distinct values among attributes of the nominal type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
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
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
1.14
Second quartile (Median) of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
92.11
Percentage of instances belonging to the most frequent class.
0.1
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.4
Entropy of the target attribute values.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
70
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.67
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
34.78
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.61
Number of attributes divided by the number of instances.
0.19
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
28.95
Percentage of instances having missing values.
1.61
Third quartile of entropy among attributes.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
10.07
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
9
The maximum number of distinct values among attributes of the nominal type.

6 tasks

106 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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