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shuttle-landing-control

shuttle-landing-control

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Author: Source: Unknown - Please cite: 1. Title: Space Shuttle Autolanding Domain 2. Sources: (a) Original source: unknown -- NASA: Mr. Roger Burke's autolander design team (b) Donor: Bojan Cestnik Jozef Stefan Institute Jamova 39 61000 Ljubljana Yugoslavia (tel.: (38)(+61) 214-399 ext.287) (c) Date: November 1988 3. Past Usage: (several, it appears) Example: Michie,D. (1988). The Fifth Generation's Unbridged Gap. In Rolf Herken (Ed.) The Universal Turing Machine: A Half-Century Survey, 466-489, Oxford University Press. 4. Relevant Information: This is a tiny database. Michie reports that Burke's group used RULEMASTER to generate comprehendable rules for determining the conditions under which an autolanding would be preferable to manual control of the spacecraft. 5. Number of Instances: 15 6. Number of Attributes: 7 (including the class attribute) 7. Attribute Information: 1. Class: noauto, auto -- that is, advise using manual/automatic control 2. STABILITY: stab, xstab 3. ERROR: XL, LX, MM, SS 4. SIGN: pp, nn 5. WIND: head, tail 6. MAGNITUDE: Low, Medium, Strong, OutOfRange 7. VISIBILITY: yes, no 8. Missing Attribute Values: -- none -- but several "don't care" values: (denoted by "*") Attribute Number: Number of Don't Care Values: 2: 2 3: 3 4: 8 5: 8 6: 5 7: 0 9. Class Distribution: 1. Use noauto control: 6 2. Use automatic control: 9% Information about the dataset CLASSTYPE: nominal CLASSINDEX: first

7 features

Class (target)nominal2 unique values
0 missing
STABILITYnominal2 unique values
2 missing
ERRORnominal4 unique values
3 missing
SIGNnominal2 unique values
8 missing
WINDnominal2 unique values
8 missing
MAGNITUDEnominal4 unique values
5 missing
VISIBILITYnominal2 unique values
0 missing

107 properties

15
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).
26
Number of missing values in the dataset.
9
Number of instances with at least one value missing.
0
Number of numeric attributes.
7
Number of nominal attributes.
0.35
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.97
Entropy of the target attribute values.
-0.25
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
9
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
1.75
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.09
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.27
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0.03
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
71.43
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.51
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.47
Number of attributes divided by the number of instances.
0.35
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
60
Percentage of instances having missing values.
1.69
Third quartile of entropy among attributes.
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
6.7
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
24.76
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.71
Average class difference between consecutive instances.
-0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
40
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.57
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
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
6
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.28
Third quartile of mutual information between the nominal attributes and the target attribute.
0.47
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
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.89
Average entropy of the attributes.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.35
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
0.05
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
-0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.67
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.06
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.4
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
0.53
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.14
Average mutual information between the nominal attributes and the target attribute.
5
Number of binary attributes.
0.04
First quartile of mutual information between the nominal attributes and the target attribute.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
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
-0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
5.11
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
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
0.98
Standard deviation of the number of distinct values among attributes of the nominal type.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.57
Average number of distinct values among the attributes of the nominal type.
First quartile of standard deviation of attributes of the numeric type.
0.47
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.4
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
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
0.6
Second quartile (Median) of entropy among attributes.
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
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
0.53
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
60
Percentage of instances belonging to the most frequent class.

11 tasks

458 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
350 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: VISIBILITY
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: VISIBILITY
209 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
179 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: VISIBILITY
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: VISIBILITY
0 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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