Abstract-The present work develops a new method to generate ensembles of classifiers that uses all available data to construct every individual classifier. The base algorithm, presented in [1], builds a decision tree in an iterative manner: The training data is divided into two subsets. In each iteration, one subset is used to grow the decision tree, starting from the decision tree produced by the previous iteration. This fully grown tree is then pruned by using the other subset. The roles of the data subsets are interchanged in every iteration. This process converges to a final tree that is stable with respect to the combined growing and pruning steps. To generate a variety of classifiers for the ensemble we randomly create the subsets needed by the iterative tree construction algorithm. The method exhibits good performance in several standard datasets at a low computational cost.
This work describes how grammatical evolution may be applied to the domain of automatic composition. Our goal is to test this technique as an alternate tool for automatic composition. The AP440 auxiliary processor will be used to play music, thus we shall use a grammar that generates AP440 melodies. Grammar evolution will use fitness functions defined from several well-known single melodies to automatically generate AP440 compositions that are expected to sound like those composed by human musicians.
This work describes how grammatical evolution may be applied to the domain of automatic composition. Our goal is to test this technique as an alternate tool for automatic composition. The AP440 auxiliary processor will be used to play music, thus we shall use a grammar that generates AP440 melodies. Grammar evolution will use fitness functions defined from several well-known single melodies to automatically generate AP440 compositions that are expected to sound like those composed by human musicians.
1 This paper compares two different approaches, followed by our research group, to efficiently run NEPs on parallel platforms, as general and transparent as possible. The vague results of jNEP (our multithreaded Java simulator for multicore desktop computers) suggests the use of massively parallel platforms (clusters of computers). The good results obtained show the scalability and viability of this last approach.
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