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breast-cancer

breast-cancer

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Author: Source: Unknown - Please cite: Citation Request: This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database. 1. Title: Breast cancer data (Michalski has used this) 2. Sources: -- Matjaz Zwitter & Milan Soklic (physicians) Institute of Oncology University Medical Center Ljubljana, Yugoslavia -- Donors: Ming Tan and Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu) -- Date: 11 July 1988 3. Past Usage: (Several: here are some) -- Michalski,R.S., Mozetic,I., Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045, Philadelphia, PA: Morgan Kaufmann. -- accuracy range: 66%-72% -- Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In Progress in Machine Learning (from the Proceedings of the 2nd European Working Session on Learning), 11-30, Bled, Yugoslavia: Sigma Press. -- 8 test results given: 65%-72% accuracy range -- Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning, 121-134, Ann Arbor, MI. -- 4 systems tested: accuracy range was 68%-73.5% -- Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. -- Assistant-86: 78% accuracy 4. Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also lymphography and primary-tumor.) This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal. 5. Number of Instances: 286 6. Number of Attributes: 9 + the class attribute 7. Attribute Information: 1. Class: no-recurrence-events, recurrence-events 2. age: 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80-89, 90-99. 3. menopause: lt40, ge40, premeno. 4. tumor-size: 0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59. 5. inv-nodes: 0-2, 3-5, 6-8, 9-11, 12-14, 15-17, 18-20, 21-23, 24-26, 27-29, 30-32, 33-35, 36-39. 6. node-caps: yes, no. 7. deg-malig: 1, 2, 3. 8. breast: left, right. 9. breast-quad: left-up, left-low, right-up, right-low, central. 10. irradiat: yes, no. 8. Missing Attribute Values: (denoted by "?") Attribute #: Number of instances with missing values: 6. 8 9. 1. 9. Class Distribution: 1. no-recurrence-events: 201 instances 2. recurrence-events: 85 instances Num Instances: 286 Num Attributes: 10 Num Continuous: 0 (Int 0 / Real 0) Num Discrete: 10 Missing values: 9 / 0.3% name type enum ints real missing distinct (1) 1 'age' Enum 100% 0% 0% 0 / 0% 6 / 2% 0% 2 'menopause' Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 3 'tumor-size' Enum 100% 0% 0% 0 / 0% 11 / 4% 0% 4 'inv-nodes' Enum 100% 0% 0% 0 / 0% 7 / 2% 0% 5 'node-caps' Enum 97% 0% 0% 8 / 3% 2 / 1% 0% 6 'deg-malig' Enum 100% 0% 0% 0 / 0% 3 / 1% 0% 7 'breast' Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 8 'breast-quad' Enum 100% 0% 0% 1 / 0% 5 / 2% 0% 9 'irradiat' Enum 100% 0% 0% 0 / 0% 2 / 1% 0% 10 'Class' Enum 100% 0% 0% 0 / 0% 2 / 1% 0%

10 features

Class (target)nominal2 unique values
0 missing
agenominal6 unique values
0 missing
menopausenominal3 unique values
0 missing
tumor-sizenominal11 unique values
0 missing
inv-nodesnominal7 unique values
0 missing
node-capsnominal2 unique values
8 missing
deg-malignominal3 unique values
0 missing
breastnominal2 unique values
0 missing
breast-quadnominal5 unique values
1 missing
irradiatnominal2 unique values
0 missing
Show all 10 features

107 properties

NumberOfInstances
286
Number of instances (rows) of the dataset.
NumberOfFeatures
10
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
9
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
9
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
10
Number of nominal attributes.
AutoCorrelation
0.57
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.62
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
CfsSubsetEval_DecisionStumpErrRate
0.3
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
0.26
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
CfsSubsetEval_NaiveBayesAUC
0.68
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
CfsSubsetEval_NaiveBayesErrRate
0.27
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
CfsSubsetEval_NaiveBayesKappa
0.27
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
0.3
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
CfsSubsetEval_kNN1NKappa
0.15
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
ClassEntropy
0.88
Entropy of the target attribute values.
DecisionStumpAUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.03
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
26.01
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
70.28
Percentage of instances belonging to the most frequent class.
MajorityClassSize
201
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.02
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.08
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
11
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.51
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.03
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
43.8
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
4.3
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.77
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
29.72
Percentage of instances belonging to the least frequent class.
MinorityClassSize
85
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
40
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
3.15
Percentage of instances having missing values.
PercentageOfMissingValues
0.31
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.89
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.32
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
2.02
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
-0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.16
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2.98
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

21 tasks

Supervised Classification on breast-cancer
767 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on breast-cancer
346 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on breast-cancer
325 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on breast-cancer
215 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on breast-cancer
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on breast-cancer
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on breast-cancer
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Class
Learning Curve on breast-cancer
216 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
Learning Curve on breast-cancer
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Data Stream Classification on breast-cancer
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Clustering on breast-cancer
0 runs
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
Clustering on breast-cancer
0 runs - estimation_procedure: 50 times Clustering
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