Data
lung-cancer

lung-cancer

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: 1. Title: Lung Cancer Data 2. Source Information: - Data was published in : Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane", Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991. - Donor: Stefan Aeberhard, stefan@coral.cs.jcu.edu.au - Date : May, 1992 3. Past Usage: - Hong, Z.Q. and Yang, J.Y. "Optimal Discriminant Plane for a Small Number of Samples and Design Method of Classifier on the Plane", Pattern Recognition, Vol. 24, No. 4, pp. 317-324, 1991. - Aeberhard, S., Coomans, D, De Vel, O. "Comparisons of Classification Methods in High Dimensional Settings", submitted to Technometrics. - Aeberhard, S., Coomans, D, De Vel, O. "The Dangers of Bias in High Dimensional Settings", submitted to pattern Recognition. 4. Relevant Information: - This data was used by Hong and Young to illustrate the power of the optimal discriminant plane even in ill-posed settings. Applying the KNN method in the resulting plane gave 77% accuracy. However, these results are strongly biased (See Aeberhard's second ref. above, or email to stefan@coral.cs.jcu.edu.au). Results obtained by Aeberhard et al. are : RDA : 62.5%, KNN 53.1%, Opt. Disc. Plane 59.4% The data described 3 types of pathological lung cancers. The Authors give no information on the individual variables nor on where the data was originally used. - In the original data 4 values for the fifth attribute were -1. These values have been changed to ? (unknown). (*) - In the original data 1 value for the 39 attribute was 4. This value has been changed to ? (unknown). (*) 5. Number of Instances: 32 6. Number of Attributes: 57 (1 class attribute, 56 predictive) 7. Attribute Information: attribute 1 is the class label. - All predictive attributes are nominal, taking on integer values 0-3 8. Missing Attribute Values: Attributes 5 and 39 (*) 9. Class Distribution: - 3 classes, 1.) 9 observations 2.) 13 " 3.) 10 " Information about the dataset CLASSTYPE: nominal CLASSINDEX: first

57 features

class (target)nominal3 unique values
0 missing
attribute2nominal2 unique values
0 missing
attribute3nominal3 unique values
0 missing
attribute4nominal4 unique values
0 missing
attribute5nominal3 unique values
4 missing
attribute6nominal2 unique values
0 missing
attribute7nominal3 unique values
0 missing
attribute8nominal3 unique values
0 missing
attribute9nominal3 unique values
0 missing
attribute10nominal3 unique values
0 missing
attribute11nominal3 unique values
0 missing
attribute12nominal3 unique values
0 missing
attribute13nominal4 unique values
0 missing
attribute14nominal3 unique values
0 missing
attribute15nominal3 unique values
0 missing
attribute16nominal3 unique values
0 missing
attribute17nominal3 unique values
0 missing
attribute18nominal2 unique values
0 missing
attribute19nominal2 unique values
0 missing
attribute20nominal3 unique values
0 missing
attribute21nominal3 unique values
0 missing
attribute22nominal2 unique values
0 missing
attribute23nominal2 unique values
0 missing
attribute24nominal2 unique values
0 missing
attribute25nominal3 unique values
0 missing
attribute26nominal3 unique values
0 missing
attribute27nominal3 unique values
0 missing
attribute28nominal2 unique values
0 missing
attribute29nominal3 unique values
0 missing
attribute30nominal3 unique values
0 missing
attribute31nominal3 unique values
0 missing
attribute32nominal3 unique values
0 missing
attribute33nominal3 unique values
0 missing
attribute34nominal3 unique values
0 missing
attribute35nominal3 unique values
0 missing
attribute36nominal3 unique values
0 missing
attribute37nominal3 unique values
0 missing
attribute38nominal3 unique values
0 missing
attribute39nominal3 unique values
1 missing
attribute40nominal3 unique values
0 missing
attribute41nominal3 unique values
0 missing
attribute42nominal3 unique values
0 missing
attribute43nominal3 unique values
0 missing
attribute44nominal3 unique values
0 missing
attribute45nominal3 unique values
0 missing
attribute46nominal3 unique values
0 missing
attribute47nominal3 unique values
0 missing
attribute48nominal2 unique values
0 missing
attribute49nominal2 unique values
0 missing
attribute50nominal3 unique values
0 missing
attribute51nominal3 unique values
0 missing
attribute52nominal3 unique values
0 missing
attribute53nominal3 unique values
0 missing
attribute54nominal3 unique values
0 missing
attribute55nominal2 unique values
0 missing
attribute56nominal2 unique values
0 missing
attribute57nominal2 unique values
0 missing

107 properties

32
Number of instances (rows) of the dataset.
57
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
5
Number of missing values in the dataset.
5
Number of instances with at least one value missing.
0
Number of numeric attributes.
57
Number of nominal attributes.
Third quartile of standard deviation of attributes of the numeric type.
0.65
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.44
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
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
0.63
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.15
Average mutual information between the nominal attributes and the target attribute.
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.59
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.23
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.95
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
13
Number of binary attributes.
0.09
First quartile of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.63
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
0.48
Standard deviation of the number of distinct values among attributes of the nominal type.
0.56
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.81
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.56
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.12
Kappa coefficient 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.
0.59
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.13
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
0.59
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
40.63
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
1.06
Second quartile (Median) of entropy among attributes.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.57
Entropy of the target attribute values.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
13
Number of instances belonging to the most frequent class.
0.2
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.81
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.59
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.63
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0.12
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.78
Number of attributes divided by the number of instances.
0.47
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
22.81
Percentage of binary attributes.
1.39
Third quartile of entropy among attributes.
0.66
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
10.22
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
15.63
Percentage of instances having missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.94
Average class difference between consecutive instances.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.27
Percentage of missing values.
Third quartile of means among attributes of the numeric type.
0.55
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
0.59
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
28.13
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0.21
Third quartile of mutual information between the nominal attributes and the target attribute.
0.63
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
0.63
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.07
Average entropy of the attributes.
9
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
Third quartile of skewness among attributes of the numeric type.
0.06
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
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.82
First quartile of entropy among attributes.

18 tasks

458 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
238 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: attribute57
162 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
183 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: attribute57
141 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: attribute57
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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