Data
lymph

lymph

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Author: Source: Unknown - Please cite: Citation Request: This lymphography 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: Lymphography Domain 2. Sources: (a) See Above. (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: 76% 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: 83% accuracy -- CN2 (99% threshold): 82% 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: 85% accuracy (estimate) -- AQ15: 80-82% 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 primary-tumor.) 5. Number of Instances: 148 6. Number of Attributes: 19 including the class attribute 7. Attribute information: --- 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: normal find, metastases, malign lymph, fibrosis 2. lymphatics: normal, arched, deformed, displaced 3. block of affere: no, yes 4. bl. of lymph. c: no, yes 5. bl. of lymph. s: no, yes 6. by pass: no, yes 7. extravasates: no, yes 8. regeneration of: no, yes 9. early uptake in: no, yes 10. lym.nodes dimin: 0-3 11. lym.nodes enlar: 1-4 12. changes in lym.: bean, oval, round 13. defect in node: no, lacunar, lac. marginal, lac. central 14. changes in node: no, lacunar, lac. margin, lac. central 15. changes in stru: no, grainy, drop-like, coarse, diluted, reticular, stripped, faint, 16. special forms: no, chalices, vesicles 17. dislocation of: no, yes 18. exclusion of no: no, yes 19. no. of nodes in: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, >=70 8. Missing Attribute Values: None 9. Class Distribution: Class: Number of Instances: normal find: 2 metastases: 81 malign lymph: 61 fibrosis: 4 Relabeled values in attribute 'lymphatics' From: '1' To: normal From: '2' To: arched From: '3' To: deformed From: '4' To: displaced Relabeled values in attribute 'block_of_affere' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'bl_of_lymph_c' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'bl_of_lymph_s' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'by_pass' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'extravasates' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'regeneration_of' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'early_uptake_in' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'changes_in_lym' From: '1' To: bean From: '2' To: oval From: '3' To: round Relabeled values in attribute 'defect_in_node' From: '1' To: no From: '2' To: lacunar From: '3' To: lac_margin From: '4' To: lac_central Relabeled values in attribute 'changes_in_node' From: '1' To: no From: '2' To: lacunar From: '3' To: lac_margin From: '4' To: lac_central Relabeled values in attribute 'changes_in_stru' From: '1' To: no From: '2' To: grainy From: '3' To: drop_like From: '4' To: coarse From: '5' To: diluted From: '6' To: reticular From: '7' To: stripped From: '8' To: faint Relabeled values in attribute 'special_forms' From: '1' To: no From: '2' To: chalices From: '3' To: vesicles Relabeled values in attribute 'dislocation_of' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'exclusion_of_no' From: '1' To: no From: '2' To: yes Relabeled values in attribute 'class' From: '1' To: normal From: '2' To: metastases From: '3' To: malign_lymph From: '4' To: fibrosis

19 features

class (target)nominal4 unique values
0 missing
lymphaticsnominal4 unique values
0 missing
block_of_afferenominal2 unique values
0 missing
bl_of_lymph_cnominal2 unique values
0 missing
bl_of_lymph_snominal2 unique values
0 missing
by_passnominal2 unique values
0 missing
extravasatesnominal2 unique values
0 missing
regeneration_ofnominal2 unique values
0 missing
early_uptake_innominal2 unique values
0 missing
lym_nodes_diminnumeric3 unique values
0 missing
lym_nodes_enlarnumeric4 unique values
0 missing
changes_in_lymnominal3 unique values
0 missing
defect_in_nodenominal4 unique values
0 missing
changes_in_nodenominal4 unique values
0 missing
changes_in_strunominal8 unique values
0 missing
special_formsnominal3 unique values
0 missing
dislocation_ofnominal2 unique values
0 missing
exclusion_of_nonominal2 unique values
0 missing
no_of_nodes_innumeric8 unique values
0 missing

107 properties

148
Number of instances (rows) of the dataset.
19
Number of attributes (columns) of the dataset.
4
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.
3
Number of numeric attributes.
16
Number of nominal attributes.
2.6
Third quartile of means among attributes of the numeric type.
0.79
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.91
DataQuality extracted from Fantail Library
2
Number of instances belonging to the least frequent class.
0.16
Percentage of numeric attributes.
0.84
Percentage of nominal attributes.
0.17
Third quartile of mutual information between the nominal attributes and the target attribute.
0.24
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.12
Average entropy of the attributes.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.74
First quartile of entropy among attributes.
5.44
DataQuality extracted from Fantail Library
0.55
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
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
9.88
Mean kurtosis among attributes of the numeric type.
0.16
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.5
First quartile of kurtosis among attributes of the numeric type.
1.91
DataQuality extracted from Fantail Library
0.79
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
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.05
Mean of means among attributes of the numeric type.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.06
First quartile of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.24
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.13
Average mutual information between the nominal attributes and the target attribute.
7.53
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.06
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
0.55
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
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
9
Number of binary attributes.
0.33
First quartile of skewness among attributes of the numeric type.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
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
1.59
Standard deviation of the number of distinct values among attributes of the nominal type.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.33
Mean skewness among attributes of the numeric type.
0.31
DataQuality extracted from Fantail Library
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.24
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
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.02
Mean standard deviation of attributes of the numeric type.
0.99
Second quartile (Median) of entropy among attributes.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
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
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.55
Percentage of instances belonging to the most frequent class.
0.27
Minimal entropy among attributes.
0.41
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.23
Entropy of the target attribute values.
0.62
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
81
Number of instances belonging to the most frequent class.
-0.5
Minimum kurtosis among attributes of the numeric type.
2.47
Second quartile (Median) of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.53
Maximum entropy among attributes.
1.06
Minimum of means among attributes of the numeric type.
0.14
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
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
29.75
Maximum kurtosis among attributes of the numeric type.
0.03
Minimal mutual information between the nominal attributes and the target attribute.
1.2
Second quartile (Median) of skewness among attributes of the numeric type.
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
2.6
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0.84
DataQuality extracted from Fantail Library
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.13
Number of attributes divided by the number of instances.
0.4
Maximum mutual information between the nominal attributes and the target attribute.
0.33
Minimum skewness among attributes of the numeric type.
0.47
Percentage of binary attributes.
1.61
Third quartile of entropy among attributes.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
9.38
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
8
The maximum number of distinct values among attributes of the nominal type.
0.31
DataQuality extracted from Fantail Library
0
Percentage of instances having missing values.
29.75
Third quartile of kurtosis among attributes of the numeric type.
0.5
Average class difference between consecutive instances.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.44
Maximum skewness among attributes of the numeric type.
0.01
Percentage of instances belonging to the least frequent class.
0
Percentage of missing values.

11 tasks

854 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
306 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
295 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
171 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
3 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
169 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
75 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
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
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