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vertebra-column

vertebra-column

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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
Show all 7 features

107 properties

NumberOfInstances
310
Number of instances (rows) of the dataset.
NumberOfFeatures
7
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
6
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
0.91
Entropy of the target attribute values.
DecisionStumpAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.22
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.02
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
67.74
Percentage of instances belonging to the most frequent class.
MajorityClassSize
210
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
38.07
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
117.92
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
4.32
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
37.56
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
7.18
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
52.86
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.12
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
18.35
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
0.16
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
17.54
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.18
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
10.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
32.26
Percentage of instances belonging to the least frequent class.
MinorityClassSize
100
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
14.29
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
85.71
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
14.29
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.21
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
24.11
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
0.35
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
12.49
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.81
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
47.44
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.64
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
15.33
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
11.77
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
74.85
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.67
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
23.31
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.57
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.24
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on vertebra-column
93 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on vertebra-column
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on vertebra-column
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Supervised Classification on vertebra-column
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Class
Clustering on vertebra-column
0 runs
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Clustering on vertebra-column
0 runs - estimation_procedure: 50 times Clustering
Define a new task

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