Data
primary-tumor

primary-tumor

active ARFF Publicly available Visibility: public Uploaded 23-04-2014 by Jan van Rijn
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Author: Source: Unknown - Please cite: Citation Request: This primary tumor 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: Primary Tumor Domain 2. Sources: (a) Source: (b) Donors: Igor Kononenko, University E.Kardelj Faculty for electrical engineering Trzaska 25 61000 Ljubljana (tel.: (38)(+61) 265-161 Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia (tel.: (38)(+61) 214-399 ext.287) (c) Date: November 1988 3. Past Usage: (sveral) 1. 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: 44% accuracy 2. Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 11-30, Sigma Press. -- Simple Bayes: 48% accuracy -- CN2 (95% threshold): 45% 3. Michalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045. Philadelphia, PA: Morgan Kaufmann. -- Experts: 42% accuracy -- AQ15: 29-41% 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 breast-cancer and lymphography.) 5. Number of Instances: 339 6. Number of Attributes: 18 including the class attribute 7. Attribute Information: (class is location of tumor) --- NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below. 1. class: lung, head & neck, esophasus, thyroid, stomach, duoden & sm.int, colon, rectum, anus, salivary glands, pancreas, gallblader, liver, kidney, bladder, testis, prostate, ovary, corpus uteri, cervix uteri, vagina, breast 2. age: <30, 30-59, >=60 3. sex: male, female 4. histologic-type: epidermoid, adeno, anaplastic 5. degree-of-diffe: well, fairly, poorly 6. bone: yes, no 7. bone-marrow: yes, no 8. lung: yes, no 9. pleura: yes, no 10. peritoneum: yes, no 11. liver: yes, no 12. brain: yes, no 13. skin: yes, no 14. neck: yes, no 15. supraclavicular: yes, no 16. axillar: yes, no 17. mediastinum: yes, no 18. abdominal: yes, no 8. Missing Attribute Values: (? indicates unknown value) Attribute#: Number of missing values 1: 0 2: 0 3: 1 4: 67 5: 155 6: 0 7: 0 8: 0 9: 0 10: 0 11: 0 12: 0 13: 1 14: 0 15: 0 16: 1 17: 0 18: 0 9. Class Distribution: Class Index: Number of instances in class: 1: 84 2: 20 3: 9 4: 14 5: 39 6: 1 7: 14 8: 6 9: 0 10: 2 11: 28 12: 16 13: 7 14: 24 15: 2 16: 1 17: 10 18: 29 19: 6 20: 2 21: 1 22: 24 Relabeled values in attribute age From: 1 To: '<30' From: 2 To: '30-59' From: 3 To: '>=60' Relabeled values in attribute sex From: 1 To: male From: 2 To: female Relabeled values in attribute histologic-type From: 1 To: epidermoid From: 2 To: adeno From: 3 To: anaplastic Relabeled values in attribute degree-of-diffe From: 1 To: well From: 2 To: fairly From: 3 To: poorly Relabeled values in attribute bone From: 1 To: yes From: 2 To: no Relabeled values in attribute bone-marrow From: 1 To: yes From: 2 To: no Relabeled values in attribute lung From: 1 To: yes From: 2 To: no Relabeled values in attribute pleura From: 1 To: yes From: 2 To: no Relabeled values in attribute peritoneum From: 1 To: yes From: 2 To: no Relabeled values in attribute liver From: 1 To: yes From: 2 To: no Relabeled values in attribute brain From: 1 To: yes From: 2 To: no Relabeled values in attribute skin From: 1 To: yes From: 2 To: no Relabeled values in attribute neck From: 1 To: yes From: 2 To: no Relabeled values in attribute supraclavicular From: 1 To: yes From: 2 To: no Relabeled values in attribute axillar From: 1 To: yes From: 2 To: no Relabeled values in attribute mediastinum From: 1 To: yes From: 2 To: no Relabeled values in attribute abdominal From: 1 To: yes From: 2 To: no Relabeled values in attribute class From: 1 To: lung From: 2 To: 'head and neck' From: 3 To: esophagus From: 4 To: thyroid From: 5 To: stomach From: 6 To: 'duoden and sm.int' From: 7 To: colon From: 8 To: rectum From: 9 To: anus From: 10 To: 'salivary glands' From: 11 To: pancreas From: 12 To: gallbladder From: 13 To: liver From: 14 To: kidney From: 15 To: bladder From: 16 To: testis From: 17 To: prostate From: 18 To: ovary From: 19 To: 'corpus uteri' From: 20 To: 'cervix uteri' From: 21 To: vagina From: 22 To: breast

18 features

class (target)nominal21 unique values
0 missing
agenominal3 unique values
0 missing
sexnominal2 unique values
1 missing
histologic-typenominal3 unique values
67 missing
degree-of-diffenominal3 unique values
155 missing
bonenominal2 unique values
0 missing
bone-marrownominal2 unique values
0 missing
lungnominal2 unique values
0 missing
pleuranominal2 unique values
0 missing
peritoneumnominal2 unique values
0 missing
livernominal2 unique values
0 missing
brainnominal2 unique values
0 missing
skinnominal2 unique values
1 missing
necknominal2 unique values
0 missing
supraclavicularnominal2 unique values
0 missing
axillarnominal2 unique values
1 missing
mediastinumnominal2 unique values
0 missing
abdominalnominal2 unique values
0 missing

62 properties

339
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
21
Number of distinct values of the target attribute (if it is nominal).
225
Number of missing values in the dataset.
207
Number of instances with at least one value missing.
0
Number of numeric attributes.
18
Number of nominal attributes.
24.78
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
0.18
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
84
Number of instances belonging to the most frequent class.
0.15
Minimal entropy among attributes.
Second quartile (Median) of skewness among attributes of the numeric type.
1.23
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
77.78
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
61.06
Percentage of instances having missing values.
0.96
Third quartile of entropy among attributes.
Maximum of means among attributes of the numeric type.
0.02
Minimal mutual information between the nominal attributes and the target attribute.
3.69
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.46
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
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
21
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
100
Percentage of nominal attributes.
0.26
Third quartile of mutual information between the nominal attributes and the target attribute.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
0.51
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
0.29
Percentage of instances belonging to the least frequent class.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.76
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
4.45
Standard deviation of the number of distinct values among attributes of the nominal type.
Mean kurtosis among attributes of the numeric type.
14
Number of binary attributes.
0.08
First quartile of mutual information between the nominal attributes and the target attribute.
Mean of means among attributes of the numeric type.
First quartile of skewness among attributes of the numeric type.
0.1
Average class difference between consecutive instances.
0.19
Average mutual information between the nominal attributes and the target attribute.
First quartile of standard deviation of attributes of the numeric type.
3.64
Entropy of the target attribute values.
3.07
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.84
Second quartile (Median) of entropy among attributes.
0.05
Number of attributes divided by the number of instances.
3.22
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of kurtosis among attributes of the numeric type.
19.46
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.

15 tasks

543 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
305 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
211 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
147 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
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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