Advances in Learning Classifier Systems: 4th International by Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, PDF

By Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)

ISBN-10: 3540437932

ISBN-13: 9783540437932

This ebook constitutes the completely refereed post-proceedings of the 4th overseas Workshop on studying Classifier structures, IWLCS 2001, held in San Francisco, CA, united states, in July 2001.
The 12 revised complete papers provided including a unique paper on a proper description of ACS have passed through rounds of reviewing and development. the 1st a part of the e-book is dedicated to theoretical problems with studying classifier platforms together with the impression of exploration process, self-adaptive classifier structures, and using classifier platforms for social simulation. the second one half is dedicated to purposes in a variety of fields similar to facts mining, inventory buying and selling, and tool distributionn networks.

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Extra resources for Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers

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With a population of classifiers in hand, the predictive values might serve the user with a metric for determining which to consider as accurate decision rules. While it would be interesting to use predictive values as a proxy for strength, similar to the way accuracy is used in XCS [10], the focus of this investigation is on using the predictive values as a means to evaluating the accuracy of classifiers extant in the population after training. 46 John H. Holmes The first consideration is where the predictive values fit into the representation of the classifier.

In less than 200 generations both fitness and communication success has attained such a level that we can state that agents are able to understand each other. 1 0 0 250 500 750 1000 Generations Fig. 2. Evolution of fitness and communication success for the minimal model that they are homogeneous: they contain 14 almost identical individuals. As an example, here are those individuals for one of the performed experiments: C1 C2 C3 C4 Agent 1 *1 01 : 01 00 0* 11 : 00 10 *0 01 : 01 10 0* 11 : 10 11 C1 C2 C3 C4 Agent 2 10 00 : 01 11 *0 10 : 10 11 01 10 : 10 01 1* 00 : 01 01 A Minimal Model of Communication for a Multi-agent Classifier System 39 Here are the resulting communication possibilities: Word sent 00 11 A1 01 01 10 11 01 00 A2 10 10 Guessed env.

These results indicate that this test is very predictive of a positive outcome and therefore very useful in assessing the presence of disease, but is a poor predictor of the absence of disease. See [2] for more complete details on the predictive values. 2 Applying the Predictive Values to LCS While the predictive values have been used extensively in medical diagnosis and signal detection, they offer a natural approach to assessing the ability of a classifier to predict class membership. Each classifier in the population after training represents a miniature diagnostic test, in that it can be applied to an unknown case to determine its class.

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Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers by Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)


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