Data
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

107 properties

286
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
9
Number of missing values in the dataset.
9
Number of instances with at least one value missing.
0
Number of numeric attributes.
10
Number of nominal attributes.
1.51
Average entropy of the attributes.
85
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.06
Third quartile of mutual information between the nominal attributes and the target attribute.
0.28
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.58
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
0.89
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.13
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
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.28
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.
0.58
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.03
Average mutual information between the nominal attributes and the target attribute.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.28
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
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
43.8
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4
Number of binary attributes.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.13
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
2.98
Standard deviation of the number of distinct values among attributes of the nominal type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.3
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.58
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
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.21
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.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.28
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
0.3
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
70.28
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
1.32
Second quartile (Median) of entropy among attributes.
0.33
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
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
201
Number of instances belonging to the most frequent class.
0.77
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.88
Entropy of the target attribute values.
3.02
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.62
Area Under the ROC Curve 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.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means 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.
-0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.08
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
40
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
11
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
3.15
Percentage of instances having missing values.
2.02
Third quartile of entropy among attributes.
0.36
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
26.01
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.31
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.57
Average class difference between consecutive instances.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
29.72
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.58
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

11 tasks

764 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
346 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
325 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
215 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Class
216 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
84 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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