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56 lines
2.3 KiB
Plaintext
Executable File
56 lines
2.3 KiB
Plaintext
Executable File
1. Title of Database: LED display domain
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2. Sources:
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(a) Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984).
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Classification and Regression Trees. Wadsworth International
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Group: Belmont, California. (see pages 43-49).
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(b) Donor: David Aha
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(c) Date: 11/10/1988
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3. Past Usage: (many)
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1. CART book (above):
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-- Optimal Bayes classification rate: 74%
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-- CART decision tree algorithm: 71% (resubstitution estimate)
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-- Nearest Neighbor Algorithm: 71%
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-- 200 training and 5000 test instances
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2. Quinlan,J.R. (1987). Simplifying Decision Trees. In International
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Journal of Man-Machine Studies (to appear).
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-- C4 decision tree algorithm: 72.6% (using pessimistic pruning)
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-- 2000 training and 500 test instances
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3. Tan,M. & Eshelman,L. (1988). Using Weighted Networks to Represent
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Classification Knowledge in Noisy Domains. In Proceedings of the
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5th International Conference on Machine Learning, 121-134, Ann
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Arbor, Michigan: Morgan Kaufmann.
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-- IWN system: 73.3% (using the And-OR classification algorithm)
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-- 400 training and 500 test cases
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4. Relevant Information Paragraph:
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This simple domain contains 7 Boolean attributes and 10 concepts,
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the set of decimal digits. Recall that LED displays contain 7
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light-emitting diodes -- hence the reason for 7 attributes. The
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problem would be easy if not for the introduction of noise. In
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this case, each attribute value has the 10% probability of having
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its value inverted.
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It's valuable to know the optimal Bayes rate for these databases.
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In this case, the misclassification rate is 26% (74% classification
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accuracy).
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5. Number of Instances: chosen by the user.
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6. Number of Attributes: 7 (all Boolean-valued)
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7. Attribute Information:
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-- All attribute values are either 0 or 1, according to whether
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the corresponding light is on or not for the decimal digit.
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-- Each attribute (excluding the class attribute, which is an
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integer ranging between 0 and 9 inclusive) has a 10% percent
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chance of being inverted.
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8. Missing Attribute Values: None
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9. Class Distribution: 10% (Theoretical)
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-- Each concept (digit) has the same theoretical probability
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distribution. The program randomly selects the attribute.
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