Exploiting Ensemble Diversity for Automatic Feature Extraction
نویسندگان
چکیده
We present an automatic method, based on a neural network ensemble, for extracting multiple, diverse and complementary sets of useful classification features from highdimensional data. We demonstrate the utility of these diverse representations for an image dataset, showing good classification accuracy and a high degree of dimensionality reduction. We then outline a number of possible extensions to the project in an evolutionary computation context.
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