Zhiqiang Yang and Sheng Zhong and Rebecca N. Department of Information Systems and Computer Science National University of Singapore. Discovery of multivalued dependencies from relations. Impact of learning set quality and size on decision tree performances. Marc Sebban and Richard Nock and Jean-Hugues Chauchat and Ricco Rakotomalala. Experimental comparisons of online and batch versions of bagging and boosting. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem. Marc Sebban and Richard Nock and Stéphane Lallich. Budgeted Learning of Naive-Bayes Classifiers. Lizotte and Omid Madani and Russell Greiner. Australian Conference on Artificial Intelligence.
MML Inference of Decision Graphs with Multi-way Joins and Dynamic Attributes. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements. MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Demsar: Machine learning by function decomposition. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making.
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.īecause of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see ). Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Input attributes are printed in lowercase. persons capacity in terms of persons to carry The model evaluates cars according to the following concept structure: Rajkovic: Expert system for decision making. Marko Bohanec ( marko.bohanec ijs.si)Ĭar Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Click here to try out the new site.ĭownload: Data Folder, Data Set DescriptionĪbstract: Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.ġ. Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns.
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