Features addition and dimensionality reduction in classification

Abstract In the paper additional features are constructed in order to increase accuracy or other precision values in the original classification task. This technique is implemented vey often in a lot of machine learning tasks of various domains of knowledge. Usually the second degrees of source features and their products are used. But this process can be continued further to higher degrees. At the same time it increases dimensionality of tasks dramatically. The balance between the dimensionality problems and new features addition is discussed in the present work. The principal component analysis is used to reduce the dimensionality. These sequential steps allow to construct new space containing new features that depend from the source parameters non-linearly. The technique is discussed on the example of the heart diseases dataset. Also functional dependencies in the medical dataset are observed.