Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- 1. Title: Car Evaluation Database
- 2. Sources:
- (a) Creator: Marko Bohanec
- (b) Donors: Marko Bohanec (marko.bohanec@ijs.si)
- Blaz Zupan (blaz.zupan@ijs.si)
- (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 %)
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement