Data
AP_Omentum_Ovary

AP_Omentum_Ovary

active ARFF Publicly available Visibility: public Uploaded 07-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: GEMLeR provides a collection of gene expression datasets that can be used for benchmarking gene expression oriented machine learning algorithms. They can be used for estimation of different quality metrics (e.g. accuracy, precision, area under ROC curve, etc.) for classification, feature selection or clustering algorithms. This repository was inspired by an increasing need in machine learning / bioinformatics communities for a collection of microarray classification problems that could be used by different researches. This way many different classification or feature selection techniques can finally be compared to eachother on the same set of problems. Origin of data Each gene expression sample in GEMLeR repository comes from a large publicly available expO (Expression Project For Oncology) repository by International Genomics Consortium. The goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. The availability of this information translates into direct benefits for patients, researchers and pharma alike. Source: expO website Although there are various other sources of gene expression data available, a decision to use data from expO repository was made because of: - consistency of tissue samples processing procedure - same microarray platform used for all samples - availability of additional information for combined genotype-phenotype studies - availability of a large number of samples for different tumor types In case of publishing material based on GEMLeR datasets, then, please note the assistance you received by using this repository. This will help others to obtain the same datasets and replicate your experiments. Please cite as follows when referring to this repository: Stiglic, G., & Kokol, P. (2010). Stability of Ranked Gene Lists in Large Microarray Analysis Studies. Journal of biomedicine biotechnology, 2010, 616358. You are also welcome to acknowledge the contribution of expO (Expression Project For Oncology) and International Genomics Consortium for providing their gene expression samples to the public.

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107 properties

275
Number of instances (rows) of the dataset.
10937
Number of attributes (columns) of the dataset.
2
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.
10936
Number of numeric attributes.
1
Number of nominal attributes.
2
The maximum number of distinct values among attributes of the nominal type.
-0.17
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.32
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.
16.58
Maximum skewness among attributes of the numeric type.
466.32
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
20.74
Third quartile of kurtosis among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
327186.27
Maximum standard deviation of attributes of the numeric type.
28
Percentage of instances belonging to the least frequent class.
99.99
Percentage of numeric attributes.
2819.52
Third quartile of means among attributes of the numeric type.
0.69
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
77
Number of instances belonging to the least frequent class.
0.01
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.25
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
24.15
Mean kurtosis among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
3.64
Third quartile of skewness among attributes of the numeric type.
0.37
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3471.32
Mean of means among attributes of the numeric type.
0.25
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.98
First quartile of kurtosis among attributes of the numeric type.
1715.06
Third quartile of standard deviation of attributes of the numeric type.
0.69
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
961.86
First quartile of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.37
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
1.33
First quartile of skewness among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.69
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.
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.12
Mean skewness among attributes of the numeric type.
631.54
First quartile of standard deviation of attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.25
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2097.1
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.37
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
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
72
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
7.03
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.86
Entropy of the target attribute values.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
198
Number of instances belonging to the most frequent class.
-0.54
Minimum kurtosis among attributes of the numeric type.
1564.9
Second quartile (Median) of means among attributes of the numeric type.
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
54.74
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.23
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
274.95
Maximum kurtosis among attributes of the numeric type.
122911.02
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.06
Second quartile (Median) of skewness among attributes of the numeric type.
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.01
Percentage of binary attributes.
932.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
39.77
Number of attributes divided by the number of instances.

7 tasks

76 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
1 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Tissue
0 runs - estimation_procedure: 33% Holdout set - target_feature: Tissue
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Tissue
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task