Improving classification performance by feature space transformations and model selection

14 Jul 2019Jose Ortiz-BejarEric S. TellezMario Graff

Improving the performance of classifiers is the realm of feature mapping, prototype selection, and kernel function transformations; these techniques aim for reducing the complexity, and also, improving the accuracy of models. In particular, our objective is to combine them to transform data's shape into another more convenient distribution; such that some simple algorithms, such as Na\"ive Bayes or k-Nearest Neighbors, can produce competitive classifiers... (read more)

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