Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,String

Download

# Instances: 625 / # Attributes: 5
HDF5 (44.5 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

balance-scale

Version mldata

0

Comment

1. Title: Balance Scale Weight & Distance Database

2. Source Information: (a) Source: Generated to model psychological experiments reported by Siegler, R. S. (1976). Three Aspects of Cognitive Development. Cognitive Psychology, 8, 481-520. (b) Donor: Tim Hume (hume@ics.uci.edu) (c) Date: 22 April 1994

3. Past Usage: (possibly different formats of this data)

4. Publications

5. Klahr, D., & Siegler, R.S. (1978). The Representation of Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in Child Development and Behavior, pp. 61-116. New York: Academic Press

6. Langley,P. (1987). A General Theory of Discrimination Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production System Models of Learning and Development, pp. 99-161. Cambridge, MA: MIT Press

7. Newell, A. (1990). Unified Theories of Cognition. Cambridge, MA: Harvard University Press

8. McClelland, J.L. (1988). Parallel Distibuted Processing: Implications for Cognition and Development. Technical Report AIP-47, Department of Psychology, Carnegie-Mellon University

9. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling Cognitive Development on Balance Scale Phenomena. Machine Learning, Vol. 16, pp. 59-88.

10. Relevant Information: This data set was generated to model psychological experimental results. Each example is classified as having the balance scale tip to the right, tip to the left, or be balanced. The attributes are the left weight, the left distance, the right weight, and the right distance. The correct way to find the class is the greater of (left-distance * left-weight) and (right-distance * right-weight). If they are equal, it is balanced.

11. Number of Instances: 625 (49 balanced, 288 left, 288 right)

12. Number of Attributes: 4 (numeric) + class name = 5

13. Attribute Information:

14. Class Name: 3 (L, B, R)

15. Left-Weight: 5 (1, 2, 3, 4, 5)

16. Left-Distance: 5 (1, 2, 3, 4, 5)

17. Right-Weight: 5 (1, 2, 3, 4, 5)

18. Right-Distance: 5 (1, 2, 3, 4, 5)

19. Missing Attribute Values: none

20. Class Distribution:

21. 46.08 percent are L

22. 07.84 percent are B

23. 46.08 percent are R

Names

left-weight,left-distance,right-weight,right-distance,class,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. nominal:L,B,R

Data (first 10 data points)

left...	left...	righ...	righ...	class
1	1	1	1	B
1	1	1	2	R
1	1	1	3	R
1	1	1	4	R
1	1	1	5	R
1	1	2	1	R
1	1	2	2	R
1	1	2	3	R
1	1	2	4	R
1	1	2	5	R
...	...	...	...	...

Description

A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).

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Publications

Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 09:57

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