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Algorithm quasi‐optimal (AQ) learning

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Abstract The algorithm quasi‐optimal (AQ) is a powerful machine learning methodology aimed at learning symbolic decision rules from a set of examples and counterexamples. It was first proposed in the late 1960s to solve the Boolean function satisfiability problem and further refined over the following decade to solve the general covering problem. In its newest implementations, it is a powerful but yet little explored methodology for symbolic machine learning classification. It has been applied to solve several problems from different domains, including the generation of individuals within an evolutionary computation framework. The current article introduces the main concepts of the AQ methodology and describes AQ for source detection(AQ4SD), a tailored implementation of the AQ methodology to solve the problem of finding the sources of atmospheric releases using distributed sensor measurements. The AQ4SD program is tested to find the sources of all the releases of the prairie grass field experiment. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Rule-Based Mining

A sample association graph from an atmospheric pollution problem.

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Pair‐wise plot of different attributes used during the optimization.

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Summary of the errors of AQ4SD for each prairie grass experiment. The atmosphere type of each experiment is color coded.

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Errors of AQ4SD divided by atmosphere type.

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Different sample prairie grass releases by atmosphere type.

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Summary of the 68 prairie grass experiments.

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An event that lies in an area of the search space which is not generalized to any of the training classes is assigned to the class it is closest to.

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Different covers generated by AQ (left) and C4.5 (right) using the same dataset.

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Statistical Learning and Exploratory Methods of the Data Sciences > Rule-Based Mining
Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery

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