Data
bridges

bridges

deactivated ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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: Source: Unknown - Please cite: 1. Title: Pittsburgh bridges There are two versions of the database: V1 contains the original examples and V2 contains descriptions after discretizing numeric properties. 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on both versions (V1 and V2) of the database -- Reich (1989) Incremental Concept Formation with Mixed Property Types Unpublished Manuscript.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 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
ERECTEDnominal4 unique values
0 missing
PURPOSEnominal4 unique values
0 missing
LENGTHnominal3 unique values
26 missing
LANESnominal4 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
0
Number of numeric attributes.
NumberOfSymbolicFeatures
12
Number of nominal attributes.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
1.87
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.81
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
RandomTreeDepth2AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
1.69
Average entropy of the attributes.
MinorityClassSize
2
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile3MutualInformation
0.55
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
0.36
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
RandomTreeDepth2ErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
1.1
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
0.47
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
RandomTreeDepth2Kappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.81
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
RandomTreeDepth3AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
0.47
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
0.36
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
RandomTreeDepth3ErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanNoiseToSignalRatio
2.62
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
2
Number of binary attributes.
Quartile1MutualInformation
0.23
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
0.47
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
RandomTreeDepth3Kappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
7.67
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
0.81
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
StdvNominalAttDistinctValues
14.63
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
0.36
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
kNN1NAUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.5
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
0.47
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
kNN1NErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
41.12
Percentage of instances belonging to the most frequent class.
MinAttributeEntropy
0.58
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
2.32
Entropy of the target attribute values.
kNN1NKappa
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
44
Number of instances belonging to the most frequent class.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
5.57
Maximum entropy among attributes.
MinMeansOfNumericAtts
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
0.36
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MinMutualInformation
0.15
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
16.67
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.11
Number of attributes divided by the number of instances.
MaxMutualInformation
1.54
Maximum mutual information between the nominal attributes and the target attribute.
MinSkewnessOfNumericAtts
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
34.58
Percentage of instances having missing values.
Quartile3AttributeEntropy
1.59
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
4.97
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
54
The maximum number of distinct values among attributes of the nominal type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
5.69
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.48
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
Show all 107 properties

19 tasks

Supervised Classification on bridges
45 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: TYPE
Supervised Classification on bridges
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - 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
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
Learning Curve on bridges
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - 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
Supervised Data Stream Classification on bridges
1 runs - estimation_procedure: Interleaved Test then Train - target_feature: TYPE
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
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
Define a new task