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bridges

bridges

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Author: Source: Unknown - Please cite: 1. Title: Pittsburgh bridges 2. Sources: -- Yoram Reich & Steven J. Fenves Department of Civil Engineering and Engineering Design Research Center Carnegie Mellon University Pittsburgh, PA 15213 Compiled from various sources. -- Donor: Yoram Reich (yoram.reich@cs.cmu.edu) -- Date: 1 August 1990 3. Past Usage: -- Reich & Fenves (1989). Incremental Learning for Capturing Design Expertise. Technical Report: EDRC 12-34-89, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA. -- Qualitative results and runs with original ordering of examples. using COBWEB. -- Reich (1989). Converging to ``Ideal'' Design Knowledge by Learning, Proceedings of the First International Workshop on Formal Methods in Engineering Design, pp: 330-349, Colorado Springs, CO, January 1990. -- Describes a new design method with Bridger (variant of COBWEB) using this domain. (Also an EDRC report: 12-35-89) -- Reich (1989) Combining Nominal and Continuous Properties in an Incremental Learning System for Design. Technical Report: EDRC 12-33-89. -- Comparison of performance of Bridger when running on both versions (V1 and V2) of the database -- Reich (1989) Incremental Concept Formation with Mixed Property Types Unpublished Manuscript. -- Results using 10 random 10-fold cross-validation test with Bridger (relative error rate): Version V1 of the database: MATERIAL 18.4%, REL-L 38.7%, SPAN 42.7%, T-OR-D 14.7%, TYPE 47.6%. Version V2 of the database: MATERIAL 24.2%, REL-L 41.7%, SPAN 39.9%, T-OR-D 14.7%, TYPE 56.5%. -- Quinlan (1989) Personal communication. -- Results of a 10-fold cross-validation test with C4.5, and with a separate decision tree for each design property obtained the following error rates on version V1 of the database: MATERIAL 15%, REL-L 32%, SPAN 32%, T-OR-D 15%, TYPE 44%. 4. Number of instances: 108 5. Relevant Information: There are two versions to the database: V1 contains the original examples and V2 contains descriptions after discretizing numeric properties. There are no ``classes'' in the domain. Rather this is a DESIGN domain where 5 properties (design description) need to be predicted based on 7 specification properties. 6. Number of Attributes: 13: 7 specifications, 5 design description, and 1 identifier (not used for the classification) 7. Attribute Information: The type field state whether a property is continuous/integer (c) or nominal (n). For properties with c,n type, the range of continuous numbers is given first and the possible values of the nominal follow the semi-colon. name type possible values comments ------------------------------------------------------------------------ 1. IDENTIF - - identifier of the examples 2. RIVER n A, M, O 3. LOCATION n 1 to 52 4. ERECTED c,n 1818-1986 ; CRAFTS, EMERGING, MATURE, MODERN 5. PURPOSE n WALK, AQUEDUCT, RR, HIGHWAY 6. LENGTH c,n 804-4558 ; SHORT, MEDIUM, LONG 7. LANES c,n 1, 2, 4, 6 ; 1, 2, 4, 6 8. CLEAR-G n N, G 9. T-OR-D n THROUGH, DECK 10. MATERIAL n WOOD, IRON, STEEL 11. SPAN n SHORT, MEDUIM, LONG 12. REL-L n S, S-F, F 13. TYPE n WOOD, SUSPEN, SIMPLE-T, ARCH, CANTILEV, CONT-T 8. More complicated attributes: One can use a hierarchical structure for the Type property. There are two options. option 1 (use examples without modification) -------- Type / / / / wood suspen arch truss / | / | cantilev cont-t simple option 2 (requires changes in the Type property - specified bellow) -------- Type / / | / / | wood suspen arch truss / / | / / | tied-a not-tied cantilev cont-t simple arch-t Change the Type property of the following examples (in both V1 and V2): E28 -> arch-t E91,E90,E84,E83,E73 -> tied-a E97,E78,E77,E75,E66,E64,E43 -> not-tied 9. Missing Attribute Values: Attribute #: # instances with missing values: 2 1 6 27 7 16 8 2 9 6 10 2 11 16 12 5 13 3 Information about the dataset CLASSTYPE: nominal CLASSINDEX: no

12 features

TYPE (target)nominal6 unique values
2 missing
IDENTIF (row identifier)nominal107 unique values
0 missing
RIVERnominal4 unique values
0 missing
LOCATIONnominal54 unique values
1 missing
ERECTEDnumeric70 unique values
0 missing
PURPOSEnominal4 unique values
0 missing
LENGTHnumeric64 unique values
26 missing
LANESnumeric4 unique values
15 missing
CLEAR-Gnominal2 unique values
2 missing
T-OR-Dnominal2 unique values
5 missing
MATERIALnominal3 unique values
2 missing
SPANnominal3 unique values
15 missing
REL-Lnominal3 unique values
5 missing
Show all 12 features

107 properties

NumberOfInstances
107
Number of instances (rows) of the dataset.
NumberOfFeatures
12
Number of attributes (columns) of the dataset.
NumberOfClasses
7
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
73
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
37
Number of instances with at least one value missing.
NumberOfNumericFeatures
3
Number of numeric attributes.
NumberOfSymbolicFeatures
9
Number of nominal attributes.
AutoCorrelation
0.48
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.77
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
0.39
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
0.42
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
0.77
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
0.39
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
0.42
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
0.77
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
0.39
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
0.42
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
2.32
Entropy of the target attribute values.
DecisionStumpAUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.11
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
4.62
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
41.12
Percentage of instances belonging to the most frequent class.
MajorityClassSize
44
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
5.57
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
3.58
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1905.23
Maximum of means among attributes of the numeric type.
MaxMutualInformation
1.54
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
54
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
1.74
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
747.49
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.72
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1.39
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
1158.44
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.5
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
2.41
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
9
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.97
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
262
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.58
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.36
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
2.63
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.15
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
-0.11
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.16
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
1.87
Percentage of instances belonging to the least frequent class.
MinorityClassSize
2
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
16.67
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
34.58
Percentage of instances having missing values.
PercentageOfMissingValues
5.69
Percentage of missing values.
PercentageOfNumericFeatures
25
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
75
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.9
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.36
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
2.63
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.25
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.11
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.16
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.28
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1567.47
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.35
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.28
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
37.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.57
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
3.58
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
1905.23
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.64
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.74
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
747.49
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
16.92
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

19 tasks

Supervised Classification on bridges
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: TYPE
Learning Curve on bridges
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: TYPE
Learning Curve on bridges
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Data Stream Classification on bridges
1 runs - estimation_procedure: Interleaved Test then Train - target_feature: TYPE
Clustering on bridges
0 runs
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
Clustering on bridges
0 runs - estimation_procedure: 50 times Clustering
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