NDLI: An Adjustable Description Quality Measure for Pattern Discovery Using the AQ Methodology

Content Provider	Springer Nature Link
Author	Kaufman, Kenneth A. Michalski, Ryszard S.
Copyright Year	2000
Abstract	In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then the primary conditions a hypothesis must satisfy are consistency and completeness with regard to the data. In real-world applications, however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid. In such situations, the problem is to determine a hypothesis that represents the best trade-off between completeness and consistency. This paper presents an approach to this problem in which a learner seeks rules optimizing a rule quality criterion that combines the rule coverage (a measure of completeness) and training accuracy (a measure of inconsistency). These factors are combined into a single rule quality measure through a lexicographical evaluation functional (LEF). The method has been implemented in the AQ18 learning system for natural induction and pattern discovery, and compared with several other methods. Experiments have shown that the proposed method can be easily tailored to different problems and can simulate different rule learners by modifying the parameter of the rule quality criterion.
Starting Page	199
Ending Page	216
Page Count	18
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	14
Issue Number	2-3
e-ISSN	15737675
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2000-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Document Preparation and Text Processing Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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