Data
ilpd

ilpd

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by Rafael Gomes Mantovani
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Bendi Venkata Ramana, M. Surendra Prasad Babu, N. B. Venkateswarlu Source: UCI Please cite: Source: 1. Bendi Venkata Ramana, ramana.bendi '@' gmail.com Associate Professor, Department of Information Technology, Aditya Instutute of Technology and Management, Tekkali - 532201, Andhra Pradesh, India. 2. Prof. M. Surendra Prasad Babu drmsprasadbabu '@' yahoo.co.in Deptartment of Computer Science & Systems Engineering, Andhra University College of Engineering, Visakhapatnam-530 003 Andhra Pradesh, India. 3.Prof. N. B. Venkateswarlu venkat_ritch '@' yahoo.com Department of Computer Science and Engineering, Aditya Instutute of Technology and Management, Tekkali - 532201, Andhra Pradesh, India. Data Set Information: This data set contains 416 liver patient records and 167 non liver patient records.The data set was collected from north east of Andhra Pradesh, India. Selector is a class label used to divide into groups(liver patient or not). This data set contains 441 male patient records and 142 female patient records. Any patient whose age exceeded 89 is listed as being of age "90". Attribute Information: 1. Age Age of the patient 2. Gender Gender of the patient 3. TB Total Bilirubin 4. DB Direct Bilirubin 5. Alkphos Alkaline Phosphotase 6. Sgpt Alamine Aminotransferase 7. Sgot Aspartate Aminotransferase 8. TP Total Protiens 9. ALB Albumin 10. A/G Ratio Albumin and Globulin Ratio 11. Selector field used to split the data into two sets (labeled by the experts) Relevant Papers: 1. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Comparative Study of Liver Patients from USA and INDIA: An Exploratory Analysis”, International Journal of Computer Science Issues, ISSN :1694-0784, May 2012. 2. Bendi Venkata Ramana, Prof. M. S. Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, No.2, ISSN : 0975-5705, PP 101-114, May 2011.

11 features

Class (target)nominal2 unique values
0 missing
V1numeric72 unique values
0 missing
V2nominal2 unique values
0 missing
V3numeric113 unique values
0 missing
V4numeric80 unique values
0 missing
V5numeric263 unique values
0 missing
V6numeric152 unique values
0 missing
V7numeric177 unique values
0 missing
V8numeric58 unique values
0 missing
V9numeric40 unique values
0 missing
V10numeric70 unique values
0 missing

107 properties

583
Number of instances (rows) of the dataset.
11
Number of attributes (columns) of the dataset.
2
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.
9
Number of numeric attributes.
2
Number of nominal attributes.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.6
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.04
First quartile of skewness among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.29
Mean skewness among attributes of the numeric type.
0.94
First quartile of standard deviation of attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.37
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
71.36
Percentage of instances belonging to the most frequent class.
82.43
Mean standard deviation of attributes of the numeric type.
0.8
Second quartile (Median) of entropy among attributes.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.86
Entropy of the target attribute values.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
416
Number of instances belonging to the most frequent class.
0.8
Minimal entropy among attributes.
11.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.8
Maximum entropy among attributes.
-0.56
Minimum kurtosis among attributes of the numeric type.
6.48
Second quartile (Median) of means among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.29
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
150.92
Maximum kurtosis among attributes of the numeric type.
0.95
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3.21
Second quartile (Median) of skewness among attributes of the numeric type.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
290.58
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
6.21
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
0
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
18.18
Percentage of binary attributes.
0.8
Third quartile of entropy among attributes.
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
181.13
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.29
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
43.87
Third quartile of kurtosis among attributes of the numeric type.
0.61
Average class difference between consecutive instances.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.55
Maximum skewness among attributes of the numeric type.
0.32
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
95.31
Third quartile of means among attributes of the numeric type.
0.6
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
288.92
Maximum standard deviation of attributes of the numeric type.
28.64
Percentage of instances belonging to the least frequent class.
81.82
Percentage of numeric attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.8
Average entropy of the attributes.
167
Number of instances belonging to the least frequent class.
18.18
Percentage of nominal attributes.
5.73
Third quartile of skewness among attributes of the numeric type.
0.15
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
30.04
Mean kurtosis among attributes of the numeric type.
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.8
First quartile of entropy among attributes.
212.78
Third quartile of standard deviation of attributes of the numeric type.
0.6
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
60.14
Mean of means among attributes of the numeric type.
0.44
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.08
First quartile of kurtosis among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Average mutual information between the nominal attributes and the target attribute.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.31
First quartile of means among attributes of the numeric type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
166.89
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Define a new task