% 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 %) % % Information about the dataset % CLASSTYPE: nominal % CLASSINDEX: last % @relation car @attribute 'buying' {vhigh,high,med,low} @attribute 'maint' {vhigh,high,med,low} @attribute 'doors' {2,3,4,5more} @attribute 'persons' {2,4,more} @attribute 'lug_boot' {small,med,big} @attribute 'safety' {low,med,high} @attribute 'class' {unacc,acc,good,vgood} @data vhigh,vhigh,2,2,small,low,unacc vhigh,vhigh,2,2,small,med,unacc vhigh,vhigh,2,2,small,high,unacc vhigh,vhigh,2,2,med,low,unacc vhigh,vhigh,2,2,med,med,unacc vhigh,vhigh,2,2,med,high,unacc vhigh,vhigh,2,2,big,low,unacc vhigh,vhigh,2,2,big,med,unacc vhigh,vhigh,2,2,big,high,unacc vhigh,vhigh,2,4,small,low,unacc vhigh,vhigh,2,4,small,med,unacc vhigh,vhigh,2,4,small,high,unacc vhigh,vhigh,2,4,med,low,unacc vhigh,vhigh,2,4,med,med,unacc 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