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Car Evaluation Data Set Download: Data Folder, Data Set Description Abstract: Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods. |
Data Set Characteristics: | Multivariate | Number of Instances: | 1728 | Area: | N/A
Attribute Characteristics: | Categorical | Number of Attributes: | 6 | Date Donated | 1997-06-01
Associated Tasks: | Classification | Missing Values? | No | Number of Web Hits: | 192242
Source: Creator: Marko Bohanec Donors: 1. Marko Bohanec (marko.bohanec '@' ijs.si) 2. Blaz Zupan (blaz.zupan '@' ijs.si) Data Set Information: 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 [Web Link]). 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. Attribute Information: Class Values: unacc, acc, good, vgood Attributes: buying: vhigh, high, med, low. maint: vhigh, high, med, low. doors: 2, 3, 4, 5more. persons: 2, 4, more. lug_boot: small, med, big. safety: low, med, high. Relevant Papers: 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. [Web Link] B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear) [Web Link] Papers That Cite This Data Set1: Qingping Tao Ph. D. MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements. 2004. [View Context]. Jianbin Tan and David L. Dowe. MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes. Australian Conference on Artificial Intelligence. 2003. [View Context]. Daniel J. Lizotte and Omid Madani and Russell Greiner. Budgeted Learning of Naive-Bayes Classifiers. UAI. 2003. [View Context]. Marc Sebban and Richard Nock and St�phane Lallich. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. Journal of Machine Learning Research, 3. 2002. [View Context]. Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001. [View Context]. Marc Sebban and Richard Nock and Jean-Hugues Chauchat and Ricco Rakotomalala. Impact of learning set quality and size on decision tree performances. Int. J. Comput. Syst. Signal, 1. 2000. [View Context]. Iztok Savnik and Peter A. Flach. Discovery of multivalued dependencies from relations. Intell. Data Anal, 4. 2000. [View Context]. Jie Cheng and Russell Greiner. Comparing Bayesian Network Classifiers. UAI. 1999. [View Context]. Zhiqiang Yang and Sheng Zhong and Rebecca N. Wright. Privacy-Preserving Classification of Customer Data without Loss of Accuracy. Computer Science Department, Stevens Institute of Technology. [View Context]. Daniel J. Lizotte and Omid Madani and Russell Greiner. Budgeted Learning, Part II: The Na#ve-Bayes Case. Department of Computing Science University of Alberta. [View Context]. Daniel J. Lizotte. Library Release Form Name of Author. Budgeted Learning of Naive Bayes Classifiers. [View Context]. Jos'e L. Balc'azar. Rules with Bounded Negations and the Coverage Inference Scheme. Dept. LSI, UPC. [View Context]. Hyunwoo Kim and Wei-Yin Loh. Classification Trees with Bivariate Linear Discriminant Node Models. Department of Statistics Department of Statistics University of Tennessee University of Wisconsin. [View Context]. Huan Liu. A Family of Efficient Rule Generators. Department of Information Systems and Computer Science National University of Singapore. [View Context]. Nikunj C. Oza and Stuart J. Russell. Online Bagging and Boosting. Computer Science Division University of California. [View Context]. Shi Zhong and Weiyu Tang and Taghi M. Khoshgoftaar. Boosted Noise Filters for Identifying Mislabeled Data. Department of Computer Science and Engineering Florida Atlantic University. [View Context]. Citation Request: Please refer to the Machine Learning Repository's citation policy

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