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rsctc2010_4

rsctc2010_4

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: Data from the RSCTC 2010 Discovery Challenge. Example datasets for 6 different problems of DNA microarray data analysis and classification. All datasets contain gene expression data characterized by values of 20,000 - 65,000 attributes. Samples are assigned to several (2-10) classes. All attributes are numeric and represent measurements from DNA microarrays. Source: http://tunedit.org/challenge/RSCTC-2010-A

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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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Var52228numeric112 unique values
0 missing
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0 missing
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0 missing
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0 missing
Var53252numeric112 unique values
0 missing
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0 missing
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0 missing
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Var54276numeric108 unique values
0 missing
Var54532numeric113 unique values
0 missing
Var5numeric110 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
Var2309numeric109 unique values
0 missing
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0 missing
Var2821numeric113 unique values
0 missing
Var3077numeric107 unique values
0 missing
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0 missing
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0 missing
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0 missing
Var4101numeric110 unique values
0 missing
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0 missing
Var4613numeric105 unique values
0 missing
Var4869numeric109 unique values
0 missing
Var5125numeric110 unique values
0 missing
Var5381numeric112 unique values
0 missing
Var5637numeric112 unique values
0 missing
Var5893numeric109 unique values
0 missing
Var6149numeric112 unique values
0 missing
Var6405numeric111 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
Var9733numeric109 unique values
0 missing
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0 missing
Var10245numeric107 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
Var12037numeric112 unique values
0 missing
Var12293numeric111 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
Var13573numeric111 unique values
0 missing
Var13829numeric110 unique values
0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
Var15365numeric108 unique values
0 missing
Var15621numeric106 unique values
0 missing
Var15877numeric108 unique values
0 missing
Var16133numeric110 unique values
0 missing
Var16389numeric112 unique values
0 missing
Var16645numeric109 unique values
0 missing
Var16901numeric108 unique values
0 missing
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0 missing
Var17413numeric109 unique values
0 missing
Var17669numeric106 unique values
0 missing
Var17925numeric111 unique values
0 missing
Var18181numeric106 unique values
0 missing
Var18437numeric111 unique values
0 missing
Var18693numeric107 unique values
0 missing
Var18949numeric106 unique values
0 missing
Var19205numeric111 unique values
0 missing
Var19461numeric109 unique values
0 missing
Var19717numeric104 unique values
0 missing
Var19973numeric105 unique values
0 missing
Var20229numeric109 unique values
0 missing
Var20485numeric107 unique values
0 missing
Var20741numeric112 unique values
0 missing
Var20997numeric112 unique values
0 missing
Var21253numeric113 unique values
0 missing
Var21509numeric101 unique values
0 missing
Var21765numeric110 unique values
0 missing
Var22021numeric110 unique values
0 missing
Var22277numeric107 unique values
0 missing
Var22533numeric112 unique values
0 missing
Var22789numeric108 unique values
0 missing
Var23045numeric102 unique values
0 missing
Var23301numeric111 unique values
0 missing
Var23557numeric110 unique values
0 missing
Var23813numeric108 unique values
0 missing
Var24069numeric108 unique values
0 missing
Var24325numeric108 unique values
0 missing
Var24581numeric109 unique values
0 missing
Var24837numeric109 unique values
0 missing
Var25093numeric109 unique values
0 missing
Var25349numeric113 unique values
0 missing
Var25605numeric110 unique values
0 missing
Var25861numeric111 unique values
0 missing
Var26117numeric110 unique values
0 missing
Var26373numeric110 unique values
0 missing
Var26629numeric108 unique values
0 missing
Var26885numeric111 unique values
0 missing
Var27141numeric105 unique values
0 missing
Var27397numeric105 unique values
0 missing
Var27653numeric110 unique values
0 missing
Var27909numeric99 unique values
0 missing
Var28165numeric113 unique values
0 missing
Var28421numeric112 unique values
0 missing
Var28677numeric110 unique values
0 missing
Var28933numeric112 unique values
0 missing
Var29189numeric110 unique values
0 missing
Var29445numeric113 unique values
0 missing
Var29701numeric110 unique values
0 missing
Var29957numeric106 unique values
0 missing
Var30213numeric112 unique values
0 missing
Var30469numeric110 unique values
0 missing
Var30725numeric112 unique values
0 missing
Var30981numeric113 unique values
0 missing
Var31237numeric103 unique values
0 missing
Var31493numeric113 unique values
0 missing
Var31749numeric112 unique values
0 missing
Var32005numeric113 unique values
0 missing
Var32261numeric110 unique values
0 missing
Var32517numeric108 unique values
0 missing
Var32773numeric113 unique values
0 missing
Var33029numeric111 unique values
0 missing
Var33285numeric109 unique values
0 missing
Var33541numeric110 unique values
0 missing
Var33797numeric108 unique values
0 missing
Var34053numeric111 unique values
0 missing
Var34309numeric112 unique values
0 missing
Var34565numeric108 unique values
0 missing
Var34821numeric108 unique values
0 missing
Var35077numeric102 unique values
0 missing
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0 missing
Var35589numeric112 unique values
0 missing
Var35845numeric103 unique values
0 missing
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0 missing
Var36357numeric108 unique values
0 missing

107 properties

113
Number of instances (rows) of the dataset.
54676
Number of attributes (columns) of the dataset.
5
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.
54675
Number of numeric attributes.
1
Number of nominal attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
17.48
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.71
Second quartile (Median) of skewness among attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
483.86
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.54
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.63
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
5
The maximum number of distinct values among attributes of the nominal type.
-2.74
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.22
Maximum skewness among attributes of the numeric type.
0.14
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2
Third quartile of kurtosis among attributes of the numeric type.
0.29
Average class difference between consecutive instances.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.69
Maximum standard deviation of attributes of the numeric type.
8.85
Percentage of instances belonging to the least frequent class.
100
Percentage of numeric attributes.
3.34
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
0.63
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
10
Number of instances belonging to the least frequent class.
0
Percentage of nominal attributes.
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
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.7
Mean kurtosis among attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.09
Third quartile of skewness among attributes of the numeric type.
0.05
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
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.22
Mean of means among attributes of the numeric type.
0.62
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.06
First quartile of kurtosis among attributes of the numeric type.
0.85
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
0.63
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.01
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.01
First quartile of means among attributes of the numeric type.
0.49
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
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.55
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.05
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.57
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
5
Average number of distinct values among the attributes of the nominal type.
0.38
First quartile of skewness among attributes of the numeric type.
0
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
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.81
Mean skewness among attributes of the numeric type.
0.4
First quartile of standard deviation of attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.05
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
0.63
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
45.13
Percentage of instances belonging to the most frequent class.
0.69
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.55
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.98
Entropy of the target attribute values.
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
51
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.74
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.9
Minimum kurtosis among attributes of the numeric type.
3.18
Second quartile (Median) of means among attributes of the numeric type.
0.55
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.63
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
107.13
Maximum kurtosis among attributes of the numeric type.
0.86
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

14 tasks

8 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Decision
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Decision
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Decision
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
Define a new task