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vertebra-column

vertebra-column

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. Mantovani
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Author: Guilherme de Alencar Barreto, Ajalmar R. da Rocha Neto, Henrique Antonio Fonseca da Mota Filho Source: [original](http://www.openml.org/d/1523) - UCI Please cite: * Dataset Title: Vertebra Column - 2 classes * Abstract: Data set containing values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondilolysthesis) or 2 classes (normal or abnormal). * Source: Guilherme de Alencar Barreto (guilherme '@' deti.ufc.br) & Ajalmar R. da Rocha Neto (ajalmar '@' ifce.edu.br), Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil. Henrique Antonio Fonseca da Mota Filho (hdamota '@' gmail.com), Hospital Monte Klinikum, Fortaleza, Ceará, Brazil. * Data Set Information: Biomedical data set built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre Médico-Chirurgical de Réadaptation des Massues, Lyon, France. The data have been organized in two different but related classification tasks. The first task consists in classifying patients as belonging to one out of three categories: Normal (100 patients), Disk Hernia (60 patients) or Spondylolisthesis (150 patients). For the second task, the categories Disk Hernia and Spondylolisthesis were merged into a single category labelled as 'abnormal'. Thus, the second task consists in classifying patients as belonging to one out of two categories: Normal (100 patients) or Abnormal (210 patients). We provide files also for use within the WEKA environment. * Attribute Information: Each patient is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (in this order): pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. The following convention is used for the class labels: DH (Disk Hernia), Spondylolisthesis (SL), Normal (NO) and Abnormal (AB). * Relevant Papers: (1) Berthonnaud, E., Dimnet, J., Roussouly, P. & Labelle, H. (2005). 'Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters', Journal of Spinal Disorders & Techniques, 18(1):40–47. (2) Rocha Neto, A. R. & Barreto, G. A. (2009). 'On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis', IEEE Latin America Transactions, 7(4):487-496. (3) Rocha Neto, A. R., Sousa, R., Barreto, G. A. & Cardoso, J. S. (2011). 'Diagnostic of Pathology on the Vertebral Column with Embedded Reject Option”, Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA'2011), Gran Canaria, Spain, Lecture Notes on Computer Science, vol. 6669, p. 588-595.

7 features

Class (target)nominal2 unique values
0 missing
V1numeric310 unique values
0 missing
V2numeric310 unique values
0 missing
V3numeric280 unique values
0 missing
V4numeric279 unique values
0 missing
V5numeric310 unique values
0 missing
V6numeric310 unique values
0 missing

107 properties

310
Number of instances (rows) of the dataset.
7
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.
6
Number of numeric attributes.
1
Number of nominal attributes.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.16
Minimum kurtosis among attributes of the numeric type.
47.44
Second quartile (Median) of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.22
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
38.07
Maximum kurtosis among attributes of the numeric type.
17.54
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
117.92
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.64
Second quartile (Median) of skewness among attributes of the numeric type.
0.69
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.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
14.29
Percentage of binary attributes.
15.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
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.
-0.18
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4.32
Maximum skewness among attributes of the numeric type.
10.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
11.77
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.86
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
37.56
Maximum standard deviation of attributes of the numeric type.
32.26
Percentage of instances belonging to the least frequent class.
85.71
Percentage of numeric attributes.
74.85
Third quartile of means among attributes of the numeric type.
0.21
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
0.26
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
Average entropy of the attributes.
100
Number of instances belonging to the least frequent class.
14.29
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.53
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
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7.18
Mean kurtosis among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.67
Third quartile of skewness among attributes of the numeric type.
0.86
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
52.86
Mean of means among attributes of the numeric type.
0.21
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.21
First quartile of kurtosis among attributes of the numeric type.
23.31
Third quartile of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
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
0.26
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
Average mutual information between the nominal attributes and the target attribute.
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
24.11
First quartile of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.53
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
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.86
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.
0.19
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.
0.35
First quartile of skewness among attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
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
0.73
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
1.12
Mean skewness among attributes of the numeric type.
12.49
First quartile of standard deviation of attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.53
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
0.24
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
67.74
Percentage of instances belonging to the most frequent class.
18.35
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.91
Entropy of the target attribute values.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
210
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.81
Second quartile (Median) of kurtosis among attributes of the numeric type.

8 tasks

93 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task