Download Applications of Learning Classifier Systems by Larry Bull (auth.), Larry Bull (eds.) PDF

By Larry Bull (auth.), Larry Bull (eds.)

This rigorously edited ebook brings jointly a desirable collection of purposes of studying Classifier platforms (LCS). The ebook demonstrates the application of this computer studying approach in contemporary real-world purposes in such domain names as facts mining, modeling and optimization, and regulate. It exhibits how the LCS method combines and exploits many delicate Computing ways right into a unmarried coherent framework to provide a higher functionality over different approaches.

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By Larry Bull (auth.), Larry Bull (eds.)

This rigorously edited ebook brings jointly a desirable collection of purposes of studying Classifier platforms (LCS). The ebook demonstrates the application of this computer studying approach in contemporary real-world purposes in such domain names as facts mining, modeling and optimization, and regulate. It exhibits how the LCS method combines and exploits many delicate Computing ways right into a unmarried coherent framework to provide a higher functionality over different approaches.

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Distribution of records in the training and testing sets. Training Set Testing Set Total Fatalities Non-fatalities Total 20736 29753 50489 20735 29754 50489 41471 59507 100978 The unequal distribution of the two classes in the FARS data is typical of clinical data in general. To ensure that EpiCS was exposed to all cases in the training and testing sets, the prevalence-based bootstrapping procedure described above was employed. EpiCS was trained using the training data described above, over 20 trials, each consisting of 30,000 iterations.

The classification performance was considerably better than all Machine Learning algorithms reported apart from the the neural network solution, which xes matched (100% classification accuracy). In the Monk's 3 test xes reached 95% accuracy in the presence of 5% noise, again equivalent to the neural network solution and marginally better than the decision tree classifiers. A version of AQ15 was reported which achieved 100% accuracy, but it had been tailored specifically to deal with the noise by over-generalising.

In this way, the performance component is analogous to a forward chaining rule-base system. All classifiers in the popUlation whose taxa match the input stream comprise a Match Set [M], even though some of these classifiers may advocate different actions. The process is equivalent to the triggering of rules, and [M] is analogous to an agenda in an expert system. From [M], the classifier with the proportionally highest strength is selected. The action of this classifier is then used as the output of the system; this process is analogous to the firing of a rule in an expert system.

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