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