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data/tanveer/car/car.names
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1. Title: Car Evaluation Database
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2. Sources:
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(a) Creator: Marko Bohanec
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(b) Donors: Marko Bohanec (marko.bohanec@ijs.si)
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Blaz Zupan (blaz.zupan@ijs.si)
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(c) Date: June, 1997
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3. Past Usage:
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The hierarchical decision model, from which this dataset is
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derived, was first presented in
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M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for
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multi-attribute decision making. In 8th Intl Workshop on Expert
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Systems and their Applications, Avignon, France. pages 59-78, 1988.
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Within machine-learning, this dataset was used for the evaluation
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of HINT (Hierarchy INduction Tool), which was proved to be able to
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completely reconstruct the original hierarchical model. This,
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together with a comparison with C4.5, is presented in
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B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by
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function decomposition. ICML-97, Nashville, TN. 1997 (to appear)
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4. Relevant Information Paragraph:
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Car Evaluation Database was derived from a simple hierarchical
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decision model originally developed for the demonstration of DEX
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(M. Bohanec, V. Rajkovic: Expert system for decision
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making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates
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cars according to the following concept structure:
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CAR car acceptability
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. PRICE overall price
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. . buying buying price
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. . maint price of the maintenance
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. TECH technical characteristics
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. . COMFORT comfort
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. . . doors number of doors
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. . . persons capacity in terms of persons to carry
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. . . lug_boot the size of luggage boot
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. . safety estimated safety of the car
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Input attributes are printed in lowercase. Besides the target
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concept (CAR), the model includes three intermediate concepts:
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PRICE, TECH, COMFORT. Every concept is in the original model
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related to its lower level descendants by a set of examples (for
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these examples sets see http://www-ai.ijs.si/BlazZupan/car.html).
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The Car Evaluation Database contains examples with the structural
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information removed, i.e., directly relates CAR to the six input
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attributes: buying, maint, doors, persons, lug_boot, safety.
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Because of known underlying concept structure, this database may be
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particularly useful for testing constructive induction and
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structure discovery methods.
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5. Number of Instances: 1728
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(instances completely cover the attribute space)
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6. Number of Attributes: 6
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7. Attribute Values:
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buying v-high, high, med, low
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maint v-high, high, med, low
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doors 2, 3, 4, 5-more
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persons 2, 4, more
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lug_boot small, med, big
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safety low, med, high
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8. Missing Attribute Values: none
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9. Class Distribution (number of instances per class)
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class N N[%]
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-----------------------------
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unacc 1210 (70.023 %)
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acc 384 (22.222 %)
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good 69 ( 3.993 %)
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v-good 65 ( 3.762 %)
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