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bridges

bridges

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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on both versions (V1 and V2) of the database -- Reich (1989) Incremental Concept Formation with Mixed Property Types Unpublished Manuscript.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

13 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

108 properties

107
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
6
Number of distinct values of the target attribute (if it is nominal).
73
Number of missing values in the dataset.
37
Number of instances with at least one value missing.
0
Number of numeric attributes.
13
Number of nominal attributes.
0
Mean of means among attributes of the numeric type.
0.49
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
First quartile of kurtosis among attributes of the numeric type.
0
Third quartile of standard deviation of attributes of the numeric type.
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
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.47
Average mutual information between the nominal attributes and the target attribute.
2.62
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
First quartile of means among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
7.82
Average number of distinct values among the attributes of the nominal type.
2
Number of binary attributes.
0.23
First quartile of mutual information between the nominal attributes and the target attribute.
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean skewness among attributes of the numeric type.
0
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
15.34
Standard deviation of the number of distinct values among attributes of the nominal type.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Mean standard deviation of attributes of the numeric type.
0
First quartile of standard deviation of attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
41.12
Percentage of instances belonging to the most frequent class.
0.58
Minimal entropy among attributes.
1.5
Second quartile (Median) of entropy among attributes.
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.32
Entropy of the target attribute values.
0.38
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
44
Number of instances belonging to the most frequent class.
0
Minimum kurtosis among attributes of the numeric type.
0
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
5.57
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.43
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum kurtosis among attributes of the numeric type.
0.15
Minimal mutual information between the nominal attributes and the target attribute.
0.36
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.58
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
The predictive accuracy obtained by always predicting the majority class.
1.54
Maximum mutual information between the nominal attributes and the target attribute.
0
Minimum skewness among attributes of the numeric type.
15.38
Percentage of binary attributes.
0
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.12
Number of attributes divided by the number of instances.
54
The maximum number of distinct values among attributes of the nominal type.
0
Minimum standard deviation of attributes of the numeric type.
34.58
Percentage of instances having missing values.
1.59
Third quartile of entropy among attributes.
0.48
Average class difference between consecutive instances.
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.97
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0
Maximum skewness among attributes of the numeric type.
0.02
Percentage of instances belonging to the least frequent class.
5.25
Percentage of missing values.
0
Third quartile of kurtosis among attributes of the numeric type.
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum standard deviation of attributes of the numeric type.
10
Number of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
0
Third quartile of means among attributes of the numeric type.
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
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.34
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.69
Average entropy of the attributes.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
100
Percentage of nominal attributes.
0.55
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.51
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Mean kurtosis among attributes of the numeric type.
0.37
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.1
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
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
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

9 tasks

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