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on both versions (V1 and V2) of the database -- Reich (1989) Incremental Concept Formation with Mixed Property Types Unpublished Manuscript.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

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
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

19 properties

107
Number of instances (rows) of the dataset.
13
Number of attributes (columns) of the dataset.
7
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.
3
Number of numeric attributes.
10
Number of nominal attributes.
15.38
Percentage of binary attributes.
34.58
Percentage of instances having missing values.
0.48
Average class difference between consecutive instances.
5.25
Percentage of missing values.
0.12
Number of attributes divided by the number of instances.
23.08
Percentage of numeric attributes.
41.12
Percentage of instances belonging to the most frequent class.
76.92
Percentage of nominal attributes.
44
Number of instances belonging to the most frequent class.
1.87
Percentage of instances belonging to the least frequent class.
2
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

10 tasks

0 runs - estimation_procedure: 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: 10 times 10-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
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