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The Algorithm

The algorithm shown in Figure 3 starts just like basic windowing: it selects a random subset of the examples, learns a theory from these examples, and tests it on the remaining examples. However, contrary to basic windowing, it does not merely add incorrectly classified examples to the window for the next iteration, but also removes examples from the window if they are covered by consistent rules. A rule is considered consistent, when it did not cover a negative example during the testing phase. Note that this does not necessarily mean that the rule is consistent with all examples in the training set because it may contradict an example that has not yet been tested at the point where MaxIncSize misclassified examples have been found. Thus apparently consistent rules have to be remembered and tested again in the next iteration. However, testing is much cheaper than learning, so we expect that removing the examples that are covered by these rules from the window should keep the window size small and thus decrease learning time.


  
Figure 3: Integrative Windowing.


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Next: Implementation Up: Integrative Windowing Previous: Integrative Windowing