caa a22 4500 475799232 CHVBK 20180406123732.0 cr unu---uuuuu 170329e20000301xx s 000 0 eng 10.1023/A:1008787919756 doi (NATIONALLICENCE)springer-10.1023/A:1008787919756 An Adjustable Description Quality Measure for Pattern Discovery Using the AQ Methodology [Elektronische Daten] [Kenneth Kaufman, Ryszard Michalski] 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however, data are often noisy, and the insistence on the full completeness and consistency of the hypothesis is no longer valid.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. Kluwer Academic Publishers, 2000 machine learning nationallicence data mining nationallicence learning from noisy data nationallicence natural induction nationallicence AQ learning nationallicence decision rules nationallicence separate and conquer nationallicence Kaufman Kenneth Machine Learning and Inference Laboratory, George Mason University, 22030, Fairfax, VA aut Michalski Ryszard Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA aut Journal of Intelligent Information Systems Kluwer Academic Publishers 14/2-3(2000-03-01), 199-216 0925-9902 14:2-3<199 2000 14 10844 https://doi.org/10.1023/A:1008787919756 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1008787919756 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kaufman Kenneth Machine Learning and Inference Laboratory, George Mason University, 22030, Fairfax, VA aut NATIONALLICENCE 700 1- Michalski Ryszard Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA aut NATIONALLICENCE 773 0- Journal of Intelligent Information Systems Kluwer Academic Publishers 14/2-3(2000-03-01), 199-216 0925-9902 14:2-3<199 2000 14 10844 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer